Papers by Shunyu Yao
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation (2025.findings-acl)
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| Challenge: | Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. |
| Approach: | They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. |
| Outcome: | The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets. |
TVShowGuess: Character Comprehension in Stories as Speaker Guessing (2022.naacl-main)
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| Challenge: | Our proposed task, TVShowGuess, builds on the scripts of TV series and takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and the dialogues. |
| Approach: | They propose a task that takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and the dialogues. |
| Outcome: | The proposed models outperform baselines, yet lag behind human performance. |
Self-Attention Networks Can Process Bounded Hierarchical Languages (2021.acl-long)
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| Challenge: | Existing models that can process formal languages with hierarchical structure are limited in their performance. |
| Approach: | They propose to use a subset of Dyck-k with depth bounded by D to train self-attention networks. |
| Outcome: | The proposed model can process Dyck-(k, D) with depth bounded by D, which better captures the hierarchical structure of natural language. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Referral Augmentation for Zero-Shot Information Retrieval (2024.findings-acl)
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| Challenge: | Referral-augmented retrieval improves zero-shot document retrieval in a variety of tasks . prior work shows sparse models struggle to reconcile with dense models . |
| Approach: | They propose a technique that concatenates document indices with referrals from other documents that cite or link to the given document. |
| Outcome: | The proposed technique outperforms generative text expansion techniques on structured tasks and improves on ACL paper retrieval. |
Personality Understanding of Fictional Characters during Book Reading (2023.acl-long)
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| Challenge: | Existing methods to predict characters' personalities have not been studied in the NLP field due to the lack of appropriate datasets mimicking the process of book reading. |
| Approach: | They propose a dataset to predict characters' personalities that uses an exhaustive vocabulary of personality traits as targets. |
| Outcome: | The proposed dataset is efficient and accurate and relies on long-term context to achieve accurate predictions for both machines and humans. |
Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)
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| Challenge: | Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments. |
| Approach: | They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics. |
| Outcome: | The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas. |
Keep CALM and Explore: Language Models for Action Generation in Text-based Games (2020.emnlp-main)
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| Challenge: | Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. |
| Approach: | They propose a Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. |
| Outcome: | The proposed model achieves a 69% improvement in average game score on unsupervised games . the proposed model is competitive with or better than other models that have access to ground truth admissible actions on half of the games tested . |
Contextual Experience Replay for Self-Improvement of Language Agents (2025.acl-long)
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| Challenge: | Existing large language model (LLM) agents fail in complex tasks without any environment-specific experiences. |
| Approach: | They propose a framework that accumulates and synthesizes past experiences into a dynamic memory buffer to enable efficient self-improvement for language agents in their context window. |
| Outcome: | The proposed framework improves performance on WebArena and VisualWebAren . it surpasses tree search method with fewer token costs and achieves state-of-the-art performance of 31.9%. |
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents (2021.naacl-main)
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| Challenge: | Recent work has used text-based games as a testbed for developing autonomous agents that operate using natural language. |
| Approach: | They propose an inverse dynamics decoder to regularize representation space and encourage exploration to reduce the amount of semantic information available to a learning agent. |
| Outcome: | The proposed model achieves high scores even in the absence of language semantics on Zork I . |
Revisiting the Roles of “Text” in Text Games (2022.findings-emnlp)
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| Challenge: | Recent work has shown that random text hashes could be complementary rather than contrasting in text games. |
| Approach: | They propose a scheme to extract contextual information into an approximate state hash as extra input for an RNN-based text agent. |
| Outcome: | The proposed scheme achieves competitive performance with state-of-the-art text agents using advanced NLU techniques such as knowledge graph and passage retrieval. |
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps (2025.findings-acl)
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| Challenge: | Existing approaches to augmented generation ignore the overlap in retrieval results . overlapping content is redundantly represented, affecting the overall efficiency. |
| Approach: | They propose a model-agnostic approach to re-augmented generation that speeds up prefilling and decoding . they propose an instruction-driven module to guide the model to more suitable ways for LLMs . |
| Outcome: | The proposed approach achieves 2.79 and 2.33 times significant acceleration on average for prefilling and decoding respectively while maintaining equal generation quality. |