Conversational Search Survey Under the Era of LLM

This is the paper list related to the newest survey paper on conversational search -- A Survey of Conversational Search, according to the structure of the paper. We will keep updating this reading list and make it much more convenient for the research community.

Feel free to contact us if you find any mistakes or have any advice.

🌟 Citation

Please kindly cite our paper if it helps your research:

@article{mo2025survey,
  title={A survey of conversational search},
  author={Mo, Fengran and Mao, Kelong and Zhao, Ziliang and Qian, Hongjin and Chen, Haonan and Cheng, Yiruo and Li, Xiaoxi and Zhu, Yutao and Dou, Zhicheng and Nie, Jian-Yun},
  journal={ACM Transactions on Information Systems},
  year={2025},
  publisher={ACM New York, NY}
}

📋 Table of Content

• Query Reformulation
  • Query Reformulation in Conversational Search
  • Analysis of Existing Datasets
  • Evaluation of Query Reformulation
• Search Clarification
  • Clarification for Conversational Retrieval
  • Web Search Clarification
  • Search Clarification for Question Answering
  • Domain-specific Search Clarification
  • Search Clarification with LLMs
• Conversational Retrieval
  • Conversation Modeling
  • Context Denoising
  • Data Augmentation
  • Interpretation in Conversational Dense Retrieval
  • Re-ranking in Conversational Search
• Generation in Conversational Search
  • Utilization of Historical Search Results
  • Context Dependency Modeling
  • Conversational Knowledge Attribution
• Domain-specific and User-centric Conversational Search
  • Domain-specific Conversational Search
  • User-centric Conversational Search
• Benchmark and Evaluation

📄 Paper List

Query Reformulation

Query Reformulation in Conversational Search

1. Selecting good expansion terms for pseudo- relevance feedback, Cao et al. SIGIR 2008. [Paper]
2. Finding good feedback documents, He et al. CIKM 2009. [Paper]
3. Acquiring lexical knowledge from query logs for query expansion in patent searching, Tannebaum et al. ICSC 2012. [Paper]
4. Can you unpack that? learning to rewrite questions-in-context, Elgohary et al. EMNLP-IJCNLP 2019. [Paper]
5. TREC cast 2019: The conversational assistance track overview, Dalton et al. arXiv 2020. [Paper]
6. Making information seeking easier: An improved pipeline for conversational search, Kumar et al. EMNLP 2020. [Paper]
7. Query resolution for conversational search with limited supervision, Voskarides et al. arXiv 2020. [Paper]
8. Question rewriting for conversational question answering, Vakulenko et al. arXiv 2020. [Paper]
9. Topic propagation in conversational search, Askari et al. arXiv 2020. [Paper]
10. Few-shot generative conversational query rewriting, Yu et al. arXiv 2020. [Paper]
11. Conversational question reformulation via sequence-to-sequence architectures and pretrained language models, Lin et al. arXiv 2020. [Paper]
12. Contextualized query embeddings for conversational search, Lin et al. arXiv 2021. [Paper]
13. Multi-stage conversational passage retrieval: An approach to fusing term importance estimation and neural query rewriting, Lin et al. ACM Trans. Inf. Syst. 2021. [Paper]
14. Question rewriting for open-domain conversational QA: best practices and limitations, Tredici et al. CIKM 2021. [Paper]
15. A comparison of question rewriting methods for conversational passage retrieval, Vakulenko et al. arXiv 2021. [Paper]
16. CONQRR: conversational query rewriting for retrieval with reinforcement learning, Wu et al. arXiv 2021. [Paper]
17. TREC cast 2022: Going beyond user ask and system retrieve with initiative and response generation, Owoicho et al. TREC 2022. [Paper]
18. Explicit query rewriting for conversational dense retrieval, Qian et al. EMNLP 2022. [Paper]
19. Reinforced question rewriting for conversational question answering, Chen et al. arXiv 2022. [Paper]
20. Explicit query rewriting for conversational dense retrieval, Qian et al. EMNLP 2022. [Paper]
21. Query2doc: Query Expansion with Large Language Models, Wang et al. arXiv 2023. [Paper]
22. Learning to relate to previous turns in conversational search, Mo et al. arXiv 2023. [Paper]
23. Large language models know your contextual search intent: A prompting framework for conversational search, Mao et al. arXiv 2023. [Paper]
24. Enhancing conversational search: Large language model-aided informative query rewriting, Ye et al. arXiv 2023. [Paper]
25. Itercqr: Iterative conversational query reformulation without human supervision, Jang et al. arXiv 2023. [Paper]
26. Convgqr: Generative query reformulation for conversational search, Mo et al. arXiv 2023. [Paper]
27. Ask optimal questions: Aligning large languagemodels with retriever’s preference in conversational search, Yoon et al. arXiv 2024. [Paper]
28. CHIQ: contextual history enhancement for improving query rewriting in conversational search, Mo et al. arXiv 2024. [Paper]

Analysis of Existing Datasets

1. Can you unpack that? learning to rewrite questions-in-context, Elgohary et al. EMNLP-IJCNLP 2019. [Paper]
2. Open-domain question answering goes conversational via question rewriting, Ananha et al. arXiv 2020. [Paper]
3. TREC cast 2019: The conversational assistance track overview, Dalton et al. arXiv 2020. [Paper]
4. TREC cast 2021: The conversational assistance track overview, Dalton et al. TREC 2021. [Paper]
5. TREC cast 2022: Going beyond user ask and system retrieve with initiative and response generation, Owoicho et al. TREC 2022. [Paper]

Evaluation of Query Reformulation

1. Can you unpack that? learning to rewrite questions-in-context, Elgohary et al. EMNLP-IJCNLP 2019. [Paper]
2. Open-domain question answering goes conversational via question rewriting, Ananha et al. arXiv 2020. [Paper]
3. Search-oriented conversational query editing, Mao et al. ACL 2023. [Paper]
4. Learning to relate to previous turns in conversational search, Mo et al. arXiv 2023. [Paper]

Search Clarification

Clarification for Conversational Retrieval

1. Asking clarifying questions in open-domain information-seeking conversations, Aliannejadi et al. arXiv 2019. [Paper]
2. Convai3: Generating clarifying questions for open-domain dialogue systems (clariq), Aliannejadi et al. arXiv 2020. [Paper]
3. Analysing the effect of clarifying questions on document ranking in conversational search, Karasakis et al. arXiv 2020. [Paper]
4. Guided transformer: Leveraging multiple external sources for representation learning in conversational search, Hashemi et al. arXiv 2020. [Paper]
5. Asking clarifying questions based on negative feedback in conversational search, Bi et al. arXiv 2021. [Paper]
6. Conversational search with mixed-initiative - asking good clarification questions backed-up by passage retrieval, Mass et al. arXiv 2021. [Paper]
7. Towards facet-driven generation of clarifying questions for conversational search， Sekulic et al. ICTIR 2021. [Paper]
8. Building and evaluating open-domain dialogue corpora with clarifying questions, Aliannejadi et al. arXiv 2021. [Paper]
9. Analysing mixed initiatives and search strategies during conversational search, Aliannejadi et al. arXiv 2021. [Paper]
10. Zero-shot clarifying question generation for conversational search, Wang et al. arXiv 2023. [Paper]
11. An in-depth investigation of user response simulation for conversational search, Wang et al. arXiv 2023. [Paper]
12. Asking multimodal clarifying questions in mixed-initiative conversational search, Yuan et al. arXiv 2024. [Paper]
13. Estimating the usefulness of clarifying questions and answers for conversational search, Sekulic et al. arXiv 2024. [Paper]

Web Search Clarification

1. Finding dimensions for queries, Dou et al. CIKM 2011. [Paper]
2. Extracting query facets from search results, Kong et al. SIGIR 2013. [Paper]
3. Extending faceted search to the general web, Kong et al. CIKM 2014. [Paper]
4. Generating clarifying questions for information retrieval, Zamani et al. WWW 2020. [Paper]
5. Analyzing and learning from user interactions for search clarification, Zamani et al. arXiv 2020. [Paper]
6. MIMICS: A large-scale data collection for search clarification, Zamani et al. arXiv 2020. [Paper]
7. Template-guided clarifying question generation for web search clarification, Wang et al. CIKM 2021. [Paper]
8. Learning multiple intent representations for search queries, Hashemi et al. CIKM 2021. [Paper]
9. Ranking clarifying questions based on predicted user engagement, Lotze et al. arXiv 2021. [Paper]
10. Generating clarifying questions with web search results, Zhao et al. SIGIR 2022. [Paper]
11. Stochastic optimization of text set generation for learning multiple query intent representations, Hashemi et al. CIKM 2022. [Paper]
12. Revisiting open domain query facet extraction and generation, Samarinas et al. ICTIR 2022. [Paper]
13. Search clarification selection via query-intent-clarification graph attention, Gao et al. ECIR 2022. [Paper]
14. Mimics-duo: Offline & online evaluation of search clarification, Tavakoli et al. arXiv 2022. [Paper]
15. Improving search clarification with structured information extracted from search results, Zhao et al. SIGKDD 2023. [Paper]
16. A comparative study of training objectives for clarification facet generation, Ni et al. arXiv 2023. [Paper]
17. Clarifying the path to user satisfaction: An investigation into clarification usefulness, Rahmani et al. arXiv 2024. [Paper]
18. Analyzing coherency in facet-based clarification prompt generation for search, Litvinov et al. arXiv 2024. [Paper]
19. Generating multi-turn clarification for web information seeking, Zhao et al. WWW 2024. [Paper]
20. Enhanced facet generation with LLM editing, Lee et al. arXiv 2024. [Paper]
21. Mining exploratory queries for conversational search, Liu et al. WWW 2024. [Paper]
22. User engagement prediction for clarification in search, Sekulic et al. arXiv 2021. [Paper]

Search Clarification for Question Answering

1. What do you mean exactly?: Analyzing clarification questions in CQA. Braslavski et al. CHIIR 2017. [Paper]
2. Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information, Rao et al. arXiv 2018. [Paper]
3. Answer-based adversarial training for generating clarification questions, Rao et al. arXiv 2019. [Paper]
4. Identifying unclear questions in community question answering websites,* Trienes et al*. arXiv 2019. [Paper]
5. Asking clarification questions in knowledge-based question answering, Xu et al. EMNLP-IJCNLP 2019. [Paper]
6. Ranking clarification questions via natural language inference, Kumar et al. arXiv 2020. [Paper]
7. Clarq: A large-scale and diverse dataset for clarification question generation, Kumar et al. arXiv 2020. [Paper]
8. Generating clarifying questions in conversational search systems, Tavakoli et al. CIKM 2020. [Paper]
9. Abg-coqa: Clarifying ambiguity in conversational question answering, Guo et al. AKBC 2021. [Paper]
10. Pseudo ambiguous and clarifying questions based on sentence structures toward clarifying question answering system, Nakano et al. DialDoc@ACL 2022. [Paper]
11. Analyzing clarification in asynchronous information-seeking conversations, Tavakoli et al. J. Assoc. Inf. Sci. Technol. 2022. [Paper]
12. Asking clarification questions to handle ambiguity in open-domain QA, Lee et al. arXiv 2023. [Paper]
13. Tree of clarifications: Answering ambiguous questions with retrieval-augmented large language models, Kim et al. arXiv 2023. [Paper]

Domain-specific Search Clarification

1. A survey on conversational recommender systems, Jannach et al. arXiv 2020. [Paper]
2. Conversational recommendation: Formulation, methods, and evaluation, Lei et al. SIGIR 2020. [Paper]
3. Advances and challenges in conversational recommender systems: A survey, Gao et al. AI Open 2021. [Paper]
4. Open-domain clarification question generation without question examples, White et al. arXiv 2021. [Paper]
5. Ask what’s missing and what’s useful: Improving clarification question generation using global knowledge, Majumder et al. arXiv 2021. [Paper]
6. Diverse and specific clarification question generation with keywords, Zhang et al. arXiv 2021. [Paper]
7. Conversational vs traditional: Comparing search behavior and outcome in legal case retrieval, Liu et al. SIGIR 2021. [Paper]
8. Learning to execute actions or ask clarification questions, Shi et al. arXiv 2022. [Paper]
9. Generating clarifying questions for query refinement in source code search, Eberhart et al. arXiv 2022. [Paper]
10. Interactive query clarification and refinement via user simulation, Erbacher et al. arXiv 2022. [Paper]
11. Query generation and buffer mechanism: Towards a better conversational agent for legal case retrieval, Liu et al. Inf. Process. Manag. 2022. [Paper]
12. Leveraging large language models in conversational recommender systems, Friedman et al. arXiv 2023. [Paper]
13. Rethinking the evaluation for conversational recommendation in the era of large language models, Wang et al. arXiv 2023. [Paper]
14. Leveraging event schema to ask clarifying questions for conversational legal case retrieval, Liu et al. CIKM 2023. [Paper]
15. Python code generation by asking clarification questions, Li et al. ACL 2023. [Paper]
16. Clarifying questions in math information retrieval, Mansouri et al. ICTIR 2023. [Paper]
17. Learning to ask clarification questions with spatial reasoning, Deng et al. SIGIR 2023. [Paper]
18. Clarifydelphi: Reinforced clarification questions with defeasibility rewards for social and moral situations, Pyatkin et al. ACL 2023. [Paper]
19. Towards asking clarification questions for information seeking on task-oriented dialogues, Feng et al. arXiv 2023. [Paper]
20. Asking the right question at the right time: Human and model uncertainty guidance to ask clarification questions, Testoni et al. arXiv 2024. [Paper]

Search Clarification with LLMs

1. Zero-shot clarifying question generation for conversational search, Wang et al. arXiv 2023. [Paper]
2. An in-depth investigation of user response simulation for conversational search, Wang et al. arXiv 2023. [Paper]
3. A comparative study of training objectives for clarification facet generation, Ni et al. arXiv 2023. [Paper]
4. Clarifygpt: Empowering llm-based code generation with intention clarification, Mu et al. arXiv 2023. [Paper]
5. Eliciting human preferences with language models, Tamkin et al. arXiv 2023. [Paper]
6. Rephrase and respond: Let large language models ask better questions for themselves, Deng et al. arXiv 2023. [Paper]
7. Generating multi-turn clarification for web information seeking, Zhao et al. WWW 2024. [Paper]
8. Enhanced facet generation with LLM editing, Lee et al. arXiv 2024. [Paper]
9. Mining exploratory queries for conversational search, Liu et al. WWW 2024. [Paper]
10. Star-gate: Teaching language models to ask clarifying questions, Andukuri et al. arXiv 2024. [Paper]

Conversational Retrieval

Conversation Modeling

1. Attentive History Selection for Conversational Question Answering, Qu et al. CIKM 2019. [Paper]
2. Open-Retrieval Conversational Question Answering, Qu et al. SIGIR 2020. [Paper]
3. CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval, Le et al. ECIR 2023. [Paper]
4. Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning, Jeong et al. ACL(Findings) 2023. [Paper]
5. A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering, Li et al. arXiv 2021. [Paper]
6. Dynamic Graph Reasoning for Conversational Open-Domain Question Answering, Li et al. TOIS 2022. [Paper]
7. Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action, Pan et al. arXiv 2024. [Paper]
8. ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval, Mao et al. EMNLP 2024. [Paper]

Context Denoising

1. Few-Shot Conversational Dense Retrieval, Yu et al. SIGIR 2021. [Paper]
2. Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval, Mao et al. SIGIR 2022. [Paper]
3. Zero-shot Query Contextualization for Conversational Search, Krasakis et al. SIGIR 2022. [Paper]
4. Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search, Mo et al. CIKM 2024. [Paper]
5. Learning to Relate to Previous Turns in Conversational Search, Mo et al. KDD 2023. [Paper]
6. History-Aware Conversational Dense Retrieval, Mo et al. ACL(Findings) 2024. [Paper]

Data Augmentation

1. Contextualized Query Embeddings for Conversational Search, Lin et al. EMNLP 2021. [Paper]
2. InstructoR: Instructing Unsupervised Conversational Dense Retrieval with Large Language Models, Jin et al. EMNLP(Findings) 2023. [Paper]
3. Saving Dense Retriever from Shortcut Dependency in Conversational Search, Kim et al. EMNLP 2022. [Paper]
4. ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval, Mao et al. EMNLP 2022. [Paper]
5. ConvSDG: Session Data Generation for Conversational Search, Mo et al. WWW(Companion Volume) 2024. [Paper]
6. CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation, Huang et al. SIGDIAL 2023. [Paper]
7. Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation, Chen et al. ACL 2024. [Paper]

Interpretation in Conversational Dense Retrieval

1. Learning Denoised and Interpretable Session Representation for Conversational Search, Mao et al. WWW 2023. [Paper]
2. Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks, Christmann et al. SIGIR 2023. [Paper]
3. Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding, Cheng et al. ACL 2024. [Paper]

Re-ranking in Conversational Search

1. monoQA: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering, Kongyoung et al. EMNLP 2022. [Paper]
2. Improving Conversational Passage Re-ranking with View Ensemble, Ju et al. SIGIR 2023. [Paper]
3. Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond, Owoicho et al. SIGIR 2023. [Paper]

Generation in Conversational Search

Utilization of Historical Search Results

1. Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action, Pan et al. ArXiv 2024. [Paper]
2. Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check, Ye et al. SIGIR 2024. [Paper]

Domain-specific and User-centric Conversational Search

Medical Domain Conversational Search

1. MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs, Xia et al. EMNLP 2022. [Paper]
2. Fast and Effective Biomedical Entity Linking Using a Dual Encoder, Bhowmik et al. ACL 2021. [Paper]
3. MedAlpaca - An Open-Source Collection of Medical Conversational {AI} Models and Training Data, Han et al. arXiv 2023. [Paper]
4. BioMistral: {A} Collection of Open-Source Pretrained Large Language Models for Medical Domains, Labrak et al. ACL 2024. [Paper]
5. Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding, Toma et al. arXiv 2023. [Paper]
6. DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation, Bao et al. arXiv 2023. [Paper]
7. Med-HALT: Medical Domain Hallucination Test for Large Language Models, Pal et al. ACL 2023. [Paper]
8. Joint Medical {LLM} and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability, Wang et al. JMLR 2023. [Paper]
9. Towards a Domain Expert Evaluation Framework for Conversational Search in Healthcare, Degachi et al. CHI 2025. [Paper]
10. MedBench: {A} Large-Scale Chinese Benchmark for Evaluating MedicalLarge Language Models, Cai et al. EMNLP 2024. [Paper]
11. Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue, Yang et al. AAAI 2024. [Paper]
12. Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries, Jin et al. WWW 2024. [Paper]
13. MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering, Alonso et al. Artificial Intelligence in Medicine. [Paper]

Financial Domain Conversational Search

1. ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering, Chen et al. EMNLP 2022. [Paper]
2. StockBabble: {A} Conversational Financial Agent to support Stock Market Investors, Sharma et al. arXiv 2021. [Paper]
3. {PACIFIC:} Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance, Deng et al. EMNLP 2022. [Paper]
4. Tab-CQA: {A} Tabular Conversational Question Answering Dataset on Financial Reports, Liu et al. ACL 2023. [Paper]
5. Conversational Financial Information Retrieval Model (ConFIRM), Choi et al. arXiv 2023. [Paper]
6. {PIXIU:} {A} Comprehensive Benchmark, Instruction Dataset and Large Language Model for Finance), Xie et al. NIPS 2023. [Paper]

Legal Domain Conversational Search

1. Conversational vs Traditional: Comparing Search Behavior and Outcome in Legal Case Retrieval, Liu et al. SIGIR 2021. [Paper]
2. Query Generation and Buffer Mechanism: Towards a better conversational agent for legal case retrieval, Liu et al. IPM 2022. [Paper]
3. Investigating Conversational Agent Action in Legal Case Retrieval, Liu et al. ECIR 2023. [Paper]
4. CLosER: Conversational Legal Longformer with Expertise-Aware Passage Response Ranker for Long Contexts, Askari et al. CIKM 2023. [Paper]

Other Domains Conversational Search

1. MMConv: An Environment for Multimodal Conversational Search across Multiple Domains, Liao et al. SIGIR 2021. [Paper]
2. MMCoQA: Conversational Question Answering over Text, Tables, and Images, Li et al. ACL 2022. [Paper]
3. MoqaGPT: Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model, Zhang et al. EMNLP 2023. [Paper]

User-centric Conversational Search

1. ConvSearch: {A} Open-Domain Conversational Search Behavior Dataset, Chu et al. arXiv 2022. [Paper]
2. Bridging the Gap: From Ad-hoc to Proactive Search in Conversations, Meng et al. SIGIR 2025. [Paper]
3. A User-Centric Benchmark for Evaluating Large Language Models, Wang et al. EMNLP 2024. [Paper]
4. Towards Human-centered Proactive Conversational Agents, Deng et al. SIGIR 2024. [Paper]
5. {TITAN} : Task-oriented Dialogues with Mixed-Initiative Interactions, Yan et al. IJCAI 2023. [Paper]
6. Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets, Abbasiantaeb et al. SIGIR 2025. [Paper]
7. Doing Personal {LAPS:} LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search, Joko et al. SIGIR 2024. [Paper]
8. How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval, Mo et al. CIKM 2024. [Paper]
9. On the Multi-turn Instruction Following for Conversational Web Agents, Deng et al. ACL 2024. [Paper]

BENCHMARK AND EVALUATION

Retrieval-based Evaluation

1. Evaluating the Cranfield Paradigm for Conversational Search Systems, Fu et al. ICTIR 2022. [Paper]
2. Ditch the Gold Standard: Re-evaluating Conversational Question Answering, Li et al. ACL 2022. [Paper]
3. Towards a more Robust Evaluation for Conversational Question Answering, Siblini et al. ACL 2021. [Paper]
4. Studying the Effectiveness of Conversational Search Refinement Through User Simulation, Salle et al. ECIR 2021. [Paper]
5. How Am {I} Doing?: Evaluating Conversational Search Systems Offline, Lipani et al. TOIS 2022. [Paper]
6. Simulating and Modeling the Risk of Conversational Search, Wang et al. TOIS 2022. [Paper]
7. An In-depth Investigation of User Response Simulation for Conversational Search, Wang et al. arXiv 2023. [Paper]
8. Exploiting Simulated User Feedback for Conversational Search: Ranking, Owoicho et al. SIGIR 2023. [Paper]
9. Leveraging User Simulation to Develop and Evaluate Conversational Information Access Agents, Bernard et al. arXiv 2023. [Paper]
10. Evaluating Mixed-initiative Conversational Search Systems via User Simulation, Sekulic et al. arXiv 2022. [Paper]
11. Analysing Mixed Initiatives and Search Strategies during Conversational Search, Aliannejadi et al. arXiv 2021. [Paper]
12. Evaluating Mixed-initiative Conversational Search Systems via User Simulation, Sekulic et al. arXiv 2022. [Paper]
13. System Initiative Prediction for Multi-turn Conversational Information Seeking, Meng et al. CIKM 2023. [Paper]
14. Priming and Actions: An Analysis in Conversational Search Systems, Fu et al. SIGIR 2023. [Paper]

Generation-based Evaluation

1. How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?, Ye et al. arXiv 2024. [Paper]
2. Towards Filling the Gap in Conversational Search: From Passage Retrieval to Conversational Response Generation, Lajewska et al. arXiv 2023. [Paper]
3. Evaluating Retrieval Quality in Retrieval-Augmented Generation, Salemi et al. SIGIR 2024. [Paper]
4. The Power of Noise: Redefining Retrieval for {RAG} Systems, Cuconasu et al. arXiv 2024. [Paper]
5. Evaluating the Retrieval Component in LLM-Based Question Answering Systems, Alinejad et al. arXiv 2024. [Paper]

User-based Evaluation

1. Towards a Domain Expert Evaluation Framework for Conversational Search in Healthcare, Degachi et al. CHI 2025. [Paper]
2. Interactions with Generative Information Retrieval Systems, Aliannejadi et al. arXiv 2024. [Paper]
3. How do people interact in conversational speech-only search tasks: A preliminary analysis, Trippas et al. CHIIR 2017. [Paper]
4. A User-Centric Benchmark for Evaluating Large Language Models, Wang et al. EMNLP 2024. [Paper]
5. Re-evaluating the Command-and-Control Paradigm in Conversational Search Interactions, Trippas et al. CIKM 2024. [Paper]
6. Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search, Ji et al. MobileHCI 2024. [Paper]