Papers by Lianhui Qin
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)
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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi
| Challenge: | Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited. |
| Approach: | They propose an inference-time policy adapter which tailors a large base model without fine-tuning it. |
| Outcome: | The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4. |
C-World: A Computer Use Agent Environment Creator (2026.acl-long)
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Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou
| Challenge: | C-World enables users to build agent environments on demand. |
| Approach: | They propose a system that enables users to build agent environments on demand. |
| Outcome: | The proposed system outperforms baselines on 119k samples and achieves Spearman = 0.883 ranking correlation with real execution. |
Counterfactual Story Reasoning and Generation (D19-1)
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| Challenge: | a desired property of AI systems is counterfactual reasoning: ability to predict causal changes in future events. |
| Approach: | They propose to rewrite a short story and a counterfactual event to make it compatible with the given counterfact. |
| Outcome: | The proposed task requires deep understanding of causal narrative chains and counterfactual invariance . the proposed dataset includes 81,407 counterfact "branches" without a rewritten storyline . |
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading (P19-1)
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Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao
| Challenge: | a new approach to contentful neural conversation is proposed . end-to-end models are effective in learning fluent responses, but their responses are often vacuous and uninformative. |
| Approach: | They propose a model that provides the conversation model with relevant text on the fly as a source of external knowledge. |
| Outcome: | The proposed model improves the informativeness and diversity of generated output compared to previous methods. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts (2022.findings-acl)
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| Challenge: | Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks. |
| Approach: | They propose a method that diversifies the generative reasoning by a mixture of expert strategy on commonsense knowledge graphs to encourage various generation outputs. |
| Outcome: | The proposed method improves diversity while achieving on par performance on two GCR benchmarks, based on both automatic and human evaluations. |
Automatic Article Commenting: the Task and Dataset (P18-2)
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| Challenge: | Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums. |
| Approach: | They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics. |
| Outcome: | The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models. |
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts (2022.naacl-main)
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Daniel Khashabi, Xinxi Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, Yejin Choi
| Challenge: | Recent work shows the surprising power of continuous prompts to language models for controlled generation and solving a wide range of tasks. |
| Approach: | They propose to extract a discrete (textual) interpretation of continuous prompts faithful to the problem they solve. |
| Outcome: | The proposed model can find prompts that solve a task while being projected to an arbitrary text with a smaller drop in accuracy. |
TuringAdvice: A Generative and Dynamic Evaluation of Language Use (2021.naacl-main)
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| Challenge: | Empirical results show that today’s language models struggle at TuringAdvice . language models are getting ever-larger, and are being trained on ever-increasing quantities of text . |
| Approach: | They propose a task task that requires models to generate helpful advice in natural language. |
| Outcome: | The proposed model outperforms even multibillion parameter models on 600k in-domain training examples. |
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation (2023.acl-long)
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Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
| Challenge: | Empirical results suggest that scale is not the only way to build commonsense capabilities. |
| Approach: | They propose a commonsense distillation framework that can achieve a competitive level of commonsensing without relying on the benefits of scale. |
| Outcome: | The proposed framework breaks the dependence on the extreme-scale teacher model with two innovations: (1) the novel adaptation of NeuroLogic Decoding to enhance the generation quality of the weak, off-the-shelf language models, and (2) self-imitation learning to iteratively learn from the model’s own enhanced commonsense acquisition capabilities. |
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning (2020.emnlp-main)
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Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi
| Challenge: | Existing methods for integrating past and future contexts are limited and require manual input. |
| Approach: | They propose an unsupervised decoding algorithm that incorporates past and future contexts using off-the-shelf, left-to-right language models and no supervision. |
| Outcome: | The proposed method outperforms unsupervised methods on abductive and counterfactual reasoning tasks. |
TIMEDIAL: Temporal Commonsense Reasoning in Dialog (2021.acl-long)
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| Challenge: | Existing studies on pre-trained language models for dialog reasoning fail to understand context correctly. |
| Approach: | They propose to use a crowd-sourced English task and a time-based task to test models' temporal reasoning abilities in dialogs. |
| Outcome: | The proposed task and crowd-sourced English challenge set show that even the best performing models struggle on this task compared to humans. |
MacGyver: Are Large Language Models Creative Problem Solvers? (2024.naacl-long)
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Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas Griffiths, Faeze Brahman
| Challenge: | a new study examines the creative problem-solving capabilities of modern LLMs . it provides insight into the constrained problem- solving capabilities of both humans and AI . |
| Approach: | They use an automatically generated dataset to compare and contrast LLMs and humans to find out their creative problem-solving abilities. |
| Outcome: | The proposed dataset compares LLMs and humans in a constrained setting . it shows that humans excel in tasks they are familiar with but struggle with domain-specific knowledge . |
Social Bias Frames: Reasoning about Social and Power Implications of Language (2020.acl-main)
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| Challenge: | Language has enormous power to project social biases and reinforce stereotypes on people. |
| Approach: | They propose a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and power differentials onto others. |
| Outcome: | The proposed model can model the pragmatic frames in which people project social biases and power differentials onto others. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations (2022.emnlp-main)
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Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, Yejin Choi
| Challenge: | Pre-trained language models struggle with consistent reasoning, and prompting methods are often noisy and inconsistent. |
| Approach: | They propose a few-shot inference method inspired by the Socratic way of conversation that generates a tree of explanations that bear logical relations between each other and frames it as a satisfiability problem. |
| Outcome: | The proposed method achieves 20% better accuracy than state-of-the-art prompting methods and performs competitively with supervised models. |
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach (2020.emnlp-main)
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| Challenge: | Existing studies on aspect-based abstractive summarization assume a small set of aspects and do not consider other diverse aspects. |
| Approach: | They propose a weak supervision construction method and an aspect modeling scheme to solve this problem. |
| Outcome: | The proposed method significantly expands the application of the task in practice. |
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2022.naacl-main)
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Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi
| Challenge: | Existing paradigms for text generation are left-to-right decoding from autoregressive language models. |
| Approach: | They propose a decoding algorithm that incorporates heuristic estimates of future cost that are efficient for large-scale language models. |
| Outcome: | The proposed method outperforms baselines on five generation tasks and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. |