Papers by Sungryull Sohn
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents (2024.findings-emnlp)
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| Challenge: | Existing work to improve LLMs' planning and reasoning capabilities has shown that they have strong priors to reason about intermediate subgoals, but their accuracy is limited especially in complex environments such as web navigation. |
| Approach: | They propose a method to adapt a pre-trained large language model (LLM) as an agent for a target domain without direct fine-tuning by extracting intents from target domain demonstrations in a highly compact form. |
| Outcome: | The proposed method significantly improves the performance of GPT-3.5, 4 and Llama-3.1-70B, 405B agents on large-scale real-website navigation benchmarks from Mind2Web and online navigation tasks with its cross-benchmark generalization from Mind2. |
Code Models are Zero-shot Precondition Reasoners (2024.naacl-long)
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Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu, Anthony Liu, Dong-Ki Kim, Dongsub Shim, Moontae Lee, Honglak Lee
| Challenge: | Existing methods to reason about action preconditions are lacking for agent to complete tasks. |
| Approach: | They propose a method to reason about action preconditions using pre-trained code models. |
| Outcome: | The proposed approach improves few-shot policy learning approaches across task-oriented dialog and embodied textworld benchmarks. |
From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning (2023.emnlp-main)
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| Challenge: | Pre-trained language models have shown impressive performance in various language tasks, but are prone to spurious correlations and illusory information. |
| Approach: | They propose to use pre-trained language models to justify decisions with formalized, coherent reasoning chains. |
| Outcome: | The proposed strategies improve coherence of rationalizations yielding state-of-the-art results on Tiered Reasoning for Intuitive Physics (TRIP). |
Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance (2026.eacl-long)
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Yao Fu, Ran Qiu, Xinhe Wang, Jacob Sansom, Sathvika Ayyappa Prabhu, Huijie Tang, Jaekyeom Kim, Sungryull Sohn, Honglak Lee
| Challenge: | Large language models (LLMs) have shown strong capabilities as task-solving agents across interactive domains, but in complex environments, auxiliary guidance may be imperfect. |
| Approach: | They propose a benchmark to measure the robustness of large language models under imperfect guidance. |
| Outcome: | The proposed benchmark compared LLM agents in navigation, cooking, and gaming in a variety of environments with auxiliary guidance and noisy or underspecified instructions extracted from demonstrations. |
A Picture is Worth a Thousand Words: Language Models Plan from Pixels (2023.emnlp-main)
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| Challenge: | Recent work uses pre-trained language models to reason about plans from text instructions in embodied visual environments. |
| Approach: | They propose to use pre-trained language models to reason about plan sequences from text instructions in embodied visual environments. |
| Outcome: | The proposed approach outperforms previous approaches on the ALFWorld and VirtualHome benchmarks. |
Unsupervised Task Graph Generation from Instructional Video Transcripts (2023.findings-acl)
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| Challenge: | Existing approaches to task graph generation use instruction-tuned language models to generate accurate task graphs. |
| Approach: | They propose an unsupervised approach that combines the reasoning capabilities of instruction-tuned language models with clustering and ranking components to generate accurate task graphs. |
| Outcome: | The proposed approach generates more accurate task graphs than a supervised learning approach on tasks from the ProceL and CrossTask datasets. |
Interactive and Expressive Code-Augmented Planning with Large Language Models (2025.acl-long)
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Anthony Zhe Liu, Xinhe Wang, Jacob Sansom, Yao Fu, Jongwook Choi, Sungryull Sohn, Jaekyeom Kim, Honglak Lee
| Challenge: | Large Language Models (LLMs) have strong abilities in common-sense reasoning and interactive decision-making, but struggle with complex, long-horizon planning tasks. |
| Approach: | They propose a code-based LLM planning approach that is code-expressive while also dynamically adapting from errors. |
| Outcome: | The proposed approach can be error-prone and insufficient for handling ambiguous or unstructured data. |
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues (2023.emnlp-main)
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Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
| Challenge: | Recent advances in task-oriented dialogue systems have limitations regarding transparency and controllability. |
| Approach: | They propose to infer the TOD-flow graph from dialog data annotated with dialog acts and integrate it with any dialogue model to improve its prediction performance, transparency, and controllability. |
| Outcome: | The proposed approach improves dialog act classification and response generation performance in the MultiWOZ and SGD benchmarks. |