Papers by Xiaochuang Han
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions (2020.acl-main)
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| Challenge: | Modern deep learning models for NLP are notoriously opaque, and this has motivated efforts to design example-specific approaches to interpret such models. |
| Approach: | They propose to use influence functions to explain models by highlighting important words in input text to provide models with an explanation. |
| Outcome: | The proposed approach is particularly useful for natural language inference, a task in which ‘saliency maps’ may not have clear interpretation. |
Understanding In-Context Learning via Supportive Pretraining Data (2023.acl-long)
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| Challenge: | In-context learning (ICL) is a form of learning that provides a handful of examples at inference time, but it is not well understood why it emerges as the model has never been specifically trained on such demonstrations. |
| Approach: | They adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL and compare it with random subsets of pretrain data. |
| Outcome: | The proposed method improves the model's ICL ability by 18% if it is continued on a small subset of pretraining data. |
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding (2024.naacl-short)
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| Challenge: | Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. |
| Approach: | They propose a context-aware decoding technique that amplifies the difference between the output probabilities when a model is used with and without context. |
| Outcome: | The proposed model significantly improves faithfulness of different LM families including OPT, GPT, LLaMA, and FLAN-T5 for summarization tasks. |
P3Sum: Preserving Author’s Perspective in News Summarization with Diffusion Language Models (2024.naacl-long)
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Yuhan Liu, Shangbin Feng, Xiaochuang Han, Vidhisha Balachandran, Chan Young Park, Sachin Kumar, Yulia Tsvetkov
| Challenge: | Existing summarization systems alter the political opinions and stances of news articles in more than 50% of summaries, misrepresenting the intent and perspectives of the authors. |
| Approach: | They propose a model-based summarization approach controlled by political perspective classifiers that preserves the political stance of a generated summary. |
| Outcome: | The proposed model outperforms state-of-the-art summarization systems and large language models by up to 13.7% in terms of success rate of stance preservation, with competitive performance on standard metrics of summarizing quality. |
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control (2023.acl-long)
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| Challenge: | Existing diffusion models for continuous-valued domains have not been adopted for text data. |
| Approach: | They propose a diffusion-based language model with two key design choices . semi-autoregressive model generates blocks of text and allows local context updates . they evaluate it on unconstrained text generation benchmarks . |
| Outcome: | The proposed model outperforms autoregressive models on unconstrained text generation benchmarks on uncontrolled text generation. |
Can LLM Graph Reasoning Generalize beyond Pattern Memorization? (2024.findings-emnlp)
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| Challenge: | Existing studies seek to enhance the graph reasoning capabilities of Large Language Models (LLMs) by specialized instruction tuning. |
| Approach: | They propose to evaluate LLM graph reasoning generalization using in-distribution settings . they propose to use three strategies to improve LLM generalization . |
| Outcome: | The proposed benchmark evaluates LLM graph reasoning generalization with in-distribution settings only . it shows that LLMs struggle to generalize across reasoning and real-world patterns . |
Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates (2021.findings-emnlp)
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| Challenge: | Existing approaches to interpret black-box models to learn spurious correlations are not well understood. |
| Approach: | They propose a procedure that leverages model interpretations to update parameters towards a plausible interpretation rather than an interpretation that relies on spurious patterns in data. |
| Outcome: | The proposed procedure outperforms baseline methods that use adversarial training in a controlled setup. |
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling (D19-1)
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| Challenge: | Contextualized word embeddings are becoming a ubiquitous component of natural language processing. |
| Approach: | They propose a domain-adaptive fine-tuning approach to pretrain on unlabeled text . they test this approach on sequence labeling in two challenging domains . |
| Outcome: | The proposed approach improves on sequence labeling in two domains: Early Modern English and Twitter. |
When One LLM Drools, Multi-LLM Collaboration Rules (2026.acl-long)
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Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Shannon Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov
| Challenge: | a single general-purpose LLM is not enough to produce a reliable output, argues this paper . a multi-LLM collaboration approach addresses reliability, democratization, and pluralism . |
| Approach: | They argue that a single general-purpose LLM is not enough to produce a reliable output . they organize existing multi-LLM collaboration methods into a hierarchy based on access and information exchange . |
| Outcome: | The proposed method addresses reliability, democratization, and pluralism challenges a single LLM fails to produce a reliable output. |
On the Zero-Shot Generalization of Machine-Generated Text Detectors (2023.findings-emnlp)
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| Challenge: | rampant proliferation of large language models generates text indistinguishable from human-written language. |
| Approach: | They train neural detectors on outputs of a new generator and test their performance on held-out generators. |
| Outcome: | The proposed detectors can be built on training data from medium-sized models. |
Fortifying Toxic Speech Detectors Against Veiled Toxicity (2020.emnlp-main)
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| Challenge: | Modern toxic speech detectors are incompetent in recognizing disguised offensive language, such as adversarial attacks that deliberately avoid known toxic lexicons. |
| Approach: | They propose a framework that fortifies existing toxic speech detectors without a large labeled corpus of veiled toxicity. |
| Outcome: | The proposed framework is aimed at fortifying existing toxic speech detectors without a large labeled corpus of disguised offensive language. |
Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too? (2023.findings-emnlp)
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| Challenge: | Large language models can perform downstream tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. |
| Approach: | They propose a human readable prompt tuning method that incorporates a fluency constraint to find a distribution of effective and fluent prompts. |
| Outcome: | The proposed method outperforms baselines by 7.0% across three tasks. |
David helps Goliath: Inference-Time Collaboration Between Small Specialized and Large General Diffusion LMs (2024.naacl-long)
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| Challenge: | Existing studies of diffusion-based language models have been conducted on a smaller scale. |
| Approach: | They propose to scale an autoregressive diffusion model from 0.4B to 13B parameters and propose techniques to improve its training and inference efficiency. |
| Outcome: | The proposed model is able to combine a large general-purpose diffusion model with smaller, but specialized and contextualized diffusion models at inference time. |
No Permanent Friends or Enemies: Tracking Relationships between Nations from News (N19-1)
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| Challenge: | Understanding complex international relations is important but challenging for civilians . topic models and neural models have been proposed to explore relations without supervision . |
| Approach: | They propose an unsupervised neural model that integrates linguistic insights into the model to infer relations between nations from news articles. |
| Outcome: | The proposed model outperforms baselines from topic models and hidden Markov models. |