Papers by Zhenting Qi
Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness (2023.emnlp-industry)
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| Challenge: | Recent studies have shown that large language models are useful, honest, harmless (HHH) however, RLHF requires high hardware resources and human efforts. |
| Approach: | They propose a framework that allows LLMs to align themselves with HHH . they use IF and reinforcement learning from human feedback to fine-tune their models . |
| Outcome: | The proposed framework achieves similar performance to RLHF and human-generated models with a minimal alignment tax. |
MuTIS: Enhancing Reasoning Efficiency through Multi Turn Intervention Sampling in Reinforcement Learning (2025.emnlp-main)
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| Challenge: | Existing methods for training large reasoning models with long chain-of-thought (CoT) are limited by the number of parameters and the complexity of the model. |
| Approach: | They propose a framework that leverages multi-turn interventions to produce concise reasoning chains and demonstrates strong scalability. |
| Outcome: | The proposed framework breaks the accuracy-efficiency trade-off by producing concise reasoning chains and demonstrating strong scalability on 7B models. |
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
OpenRT: An Open-source Framework for Reasoning Over Tabular Data (2023.acl-demo)
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| Challenge: | Existing table pre-training methods are benchmarked on a limited number of datasets with varying configurations, resulting in a lack of unified, standardized, fair, and comprehensive comparison between methods. |
| Approach: | They propose to use OpenRT to reproduce existing table pre-training models and develop new models quickly. |
| Outcome: | The proposed framework reproduces existing table pre-training models and compares them against four question answering, one fact checking, and one faithful text generation datasets. |
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains (2024.findings-emnlp)
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Simeng Han, Aaron Yu, Rui Shen, Zhenting Qi, Martin Riddell, Wenfei Zhou, Yujie Qiao, Yilun Zhao, Semih Yavuz, Ye Liu, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Dragomir Radev, Rex Ying, Arman Cohan
| Challenge: | Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales. |
| Approach: | They propose to annotate a human-annotated dataset consisting of diverse and complex reasoning chains for a set of realistic logical reasoning stories also written by humans. |
| Outcome: | The proposed model outperforms existing methods on understanding the capabilities of LLMs in logical reasoning by 10% or more. |
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (2022.emnlp-main)
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| Challenge: | Existing models with table-specific architectures and pre-training methods perform well on understanding table structures but lack table reasoning skills. |
| Approach: | They propose to pre-train tables with table reasoning skills without complex architectures . they define 7 table reasoning skill, and then pre-teach them to generate tables . |
| Outcome: | The proposed model improves on four tasks and is available on github. |
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching (2023.emnlp-industry)
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| Challenge: | Low-Rank Adaptation (LoRA) has been used to adapt Large Language Models to a variety of tasks, but it requires substantial computational resources to perform. |
| Approach: | They propose a low-rank adaptive learning approach that leverages LoRA's in-context learning capability through prompt matching via reinforcement learning in resource-constrained environments. |
| Outcome: | The proposed model improves LoRA performance on evaluation metrics and utilises consumer-grade GPU resources. |
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations (2023.acl-long)
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Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev
| Challenge: | Existing Table QA models are vulnerable to task-specific perturbations, such as replacing key question entities or shuffling table columns. |
| Approach: | They propose to use large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models. |
| Outcome: | The proposed model significantly improves on existing Table QA models against human-annotated adversarial perturbations. |
Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models (2026.acl-long)
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| Challenge: | Recent studies have highlighted weak-to-strong generalization, where a strong model trained only on a weak model’s labels surpasses the weak model in task performance. |
| Approach: | They propose two fundamental fine-tuning strategies that leverage trustworthiness regularization during the fine-uning of the weak model and the weak-to-strong transfer to improve trustworthy. |
| Outcome: | The proposed models show that they can generalize robustness, fairness, and privacy better when trained on weak models than models trained on strong models. |
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control (2023.eacl-main)
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| Challenge: | Existing models for LT2T generation focus on surface-level realizations without much logical inference. |
| Approach: | They propose a model that uses logic forms as fact verifiers and content planners to control LT2T generation. |
| Outcome: | Experimental results show that the proposed model addresses unfaithfulness and diversity issues simultaneously. |
SaFER: A Robust and Efficient Framework for Fine-tuning BERT-based Classifier with Noisy Labels (2023.acl-industry)
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| Challenge: | Existing noise-handling methods could not improve performance of BERT on noisy datasets . existing methods could only improve performance on noisy data, authors say . |
| Approach: | They propose a fine-tuning framework for BERT-based text classifiers that combats label noises without access to clean data for training or validation. |
| Outcome: | The proposed framework achieves superior performance on multiple text classification benchmarks. |
QTSumm: Query-Focused Summarization over Tabular Data (2023.emnlp-main)
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Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan
| Challenge: | Existing text generation systems that can provide accurate table summaries can facilitate more efficient access to relevant data insights. |
| Approach: | They propose a query-focused task where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored table summary. |
| Outcome: | The proposed method improves existing baselines on table-to-text generation and large language models by concatenating generated facts to the model input. |