Papers by Jiani Huang
TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games (2025.emnlp-main)
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| Challenge: | evaluating large language models' reasoning abilities via detective stories is often infeasible due to the large answer space and diverse reasoning types presented by its questions. |
| Approach: | They propose a framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gameplay of detective games Ace Attorney and Danganronpa. |
| Outcome: | The proposed framework and dataset are based on the detective games Ace Attorney and Danganronpa and show that they are more efficient than current strategies for enhancing deductive reasoning. |
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning (2026.acl-long)
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| Challenge: | Existing reasoning-augmented systems that handle complex queries are lacking . we present a framework that enhances LLM-based recommendation assistants . |
| Approach: | They propose a reinforcement fine-tuning framework that enhances LLM-based recommendation . they use a dual-graph Enhanced Reward Shaping framework to integrate recommendation metrics . |
| Outcome: | The proposed framework outperforms state-of-the-art recommendations and preserves core abilities. |
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming (2023.findings-acl)
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| Challenge: | Pre-trained large language models struggle to perform logical reasoning reliably despite advances in scale and compositionality. |
| Approach: | They propose a Differentiable Symbolic Reasoning framework that uses symbolic programming to improve LMs' logical reasoning abilities. |
| Outcome: | The proposed framework outperforms competitive baselines when faced with systematic changes in sequence length. |