Papers by Jiaao Chen
Unlearn What You Want to Forget: Efficient Unlearning for LLMs (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) can be used to memorize a vast amount of data, but can suffer from privacy issues and data protection violations. |
| Approach: | They propose an efficient unlearning framework that could update LLMs without retraining them . they introduce lightweight unlearning layers learned with a selective teacher-student objective into transformers . |
| Outcome: | The proposed framework could update LLMs without having to retrain the whole model after data removals. |
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability (2021.acl-long)
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| Challenge: | Using data augmentation to fine-tune pre-trained models with task-specific data has been shown to be ineffective and redundant during fine-timing. |
| Approach: | They propose a data augmentation technique to regularize pre-trained models and encourage them to learn more generalizable features by dropping contiguous spans during training. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark and consistently exhibits superior generalization performances on out-of distribution and challenging counterexamples. |
Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs (2021.naacl-main)
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| Challenge: | Abstractive conversation summarization has received much attention, but it suffers from insufficient, redundant, or incorrect content due to the unstructured and complex characteristics of human-human interactions. |
| Approach: | They propose to model rich structures in conversations for more precise and accurate conversation summarization by incorporating discourse relations between utterances and action triples in utterrances and designing a multi-granularity decoder to generate summaries by combining all levels of information. |
| Outcome: | The proposed models outperform state-of-the-art methods and generalize well in other domains in terms of automatic evaluations and human judgments. |
WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning (2026.findings-eacl)
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| Challenge: | Existing web agents relying on supervised fine-tuning struggle with generalization and robustness due to insufficient reasoning capabilities when handling the inherently dynamic nature of web interactions. |
| Approach: | They propose a large language model-empowered web agent that trains using a rule-based reinforcement learning framework to enhance single-step reasoning and planning for business-oriented web navigation tasks. |
| Outcome: | The proposed agent outperforms baseline LLM-based agents on the WorkArena benchmark by 10.26–16.59%. |
Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (2023.emnlp-main)
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| Challenge: | Recent advances in scale have enabled large language models to perform NLP tasks zero-shot . however, it is not known whether ChatGPT can serve as a generalist model that can perform many NLP jobs zero- shot. |
| Approach: | They empirically evaluate ChatGPT's zero-shot learning ability on 20 popular NLP datasets . they find it performs well on many tasks favoring reasoning abilities . |
| Outcome: | The proposed model can perform many NLP tasks zero-shot without adaptation on downstream data. |
DcLM: Output Length Control of Large Language Models via Dynamic Length Markers (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have limited awareness of output length, making it difficult to satisfy precise length requirements. |
| Approach: | They propose a model-agnostic approach that introduces dynamic length markers to guide length-controllable outputs. |
| Outcome: | The proposed method significantly reduces length deviation across multiple datasets. |
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named Entity Recognition (NER) systems perform well on in-distribution data, but perform poorly on examples drawn from a shifted distribution. |
| Approach: | They propose to use expert-guided heuristics to change entity tokens and their contexts to alter their entity types as adversarial attacks. |
| Outcome: | The proposed model significantly improves performance on the challenging set and out-of-domain generalization. |
VALUE: Understanding Dialect Disparity in NLU (2022.acl-long)
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| Challenge: | English Natural Language Understanding systems outperform humans on benchmarks like GLUE and SuperGLUE, but they only use textbook Standard American English (SAE) . fewer studies have considered the effects of dialectal differences on performance . |
| Approach: | They propose a benchmark to evaluate the performance of English natural language understanding systems using a set of lexical and morphosyntactic transformation rules. |
| Outcome: | The proposed model outperforms humans on GLUE and SuperGLUE, but only on standard American English . the proposed model recruits fluent speakers of African American vernacular english to validate each feature transformation . |
Continual Learning for Text Classification with Information Disentanglement Based Regularization (2021.naacl-main)
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| Challenge: | Existing continual learning methods focus on preserving knowledge from previous tasks . Continual learning is a useful tool for learning over time, but it is not always possible to generalize to new tasks. |
| Approach: | They propose a disentanglement-based regularization method for continual learning on text classification that disentangles text hidden spaces into generic representations and regularizes them differently to constrain knowledge required to generalize. |
| Outcome: | The proposed method disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task. |
Compositional Data Augmentation for Abstractive Conversation Summarization (2023.acl-long)
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| Challenge: | Abstractive conversation summarization systems rely on large-scale annotated summaries, but collecting and annotating these conversations can be time-consuming and labor-intensive. |
| Approach: | They propose a method for generating diverse and high-quality pairs of conversations and summaries by extracting conversation structures and organizing meaningful conversation snippets. |
| Outcome: | The proposed method outperforms baseline methods on SAMSum and DialogSum datasets and achieves a 10% increase in ROUGE scores with limited data. |
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (2020.acl-main)
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| Challenge: | Xie et al., 2016) demonstrate that semi-supervised learning models suffer from over-fitting when there is only limited labeled data. |
| Approach: | They propose a semi-supervised learning method for text classification using a data augmentation method called TMix. |
| Outcome: | The proposed method outperforms pre-trained and fine-tuned models on several text classification benchmarks. |
When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain (2022.emnlp-main)
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Raj Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah, Wendi Du, Sudheer Chava, Natraj Raman, Charese Smiley, Jiaao Chen, Diyi Yang
| Challenge: | Pre-trained language models have shown impressive performance on a variety of tasks and domains. |
| Approach: | They propose a domain specific financial LANGuage model which uses financial keywords and phrases for better masking. |
| Outcome: | The proposed model outperforms existing models on a variety of tasks and domains. |
Focus on the Action: Learning to Highlight and Summarize Jointly for Email To-Do Items Summarization (2022.findings-acl)
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| Challenge: | Existing to-do item generation models focus on generating action mentions to provide more structured summaries of email text. |
| Approach: | They propose a learning to highlight and summarize framework to learn to identify the most salient text and actions and incorporate these structured representations to generate more faithful to-do items. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance in terms of evaluation and human judgement. |
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise (2023.emnlp-main)
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| Challenge: | Existing diffusion models have limitations in modeling discrete data, e.g., languages . we present a novel diffusion model for language modeling inspired by linguistic features in languages based on iterative denoising . |
| Approach: | They propose a method that iteratively denoises and adds corruptions to the textual data through soft-masking to better noise it. |
| Outcome: | The proposed model achieves better generation quality and lower training cost than current models with better performance. |
Local Additivity Based Data Augmentation for Semi-supervised NER (2020.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. |
| Approach: | They propose a Local Additivity based Data Augmentation method for semi-supervised Named Entity Recognition (NER) that creates virtual samples by interpolating sequences close to each other. |
| Outcome: | The proposed method improves both entity and context learning by adding to training data and extending it to semi-supervised setting. |
Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization (2021.emnlp-main)
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| Challenge: | Abstractive conversation summarization models heavily rely on human-annotated summaries. |
| Approach: | They propose a set of Conversational Data Augmentation methods for semi-supervised abstractive conversation summarization that use random swapping/deletion to perturb the discourse relations inside conversations and dialogue-acts-guided insertion to interrupt the development of conversations. |
| Outcome: | The proposed methods over several state-of-the-art datasets show that they are more efficient than previous methods. |
Controllable Conversation Generation with Conversation Structures via Diffusion Models (2023.findings-acl)
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| Challenge: | Current generation models fail to effectively utilize rich linguistic and world knowledge to generate coherent long text. |
| Approach: | They propose a conversation generation framework that incorporates human knowledge and conversation structures with both controllability and interpretability for better conversation generation. |
| Outcome: | The proposed framework incorporates human knowledge and conversation structures with both controllability and interpretability for better conversation generation. |
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)
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| Challenge: | Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. |
| Approach: | They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs. |
| Outcome: | The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
Skills-in-Context: Unlocking Compositionality in Large Language Models (2024.findings-emnlp)
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| Challenge: | eliciting compositional generalization capabilities in large language models is challenging for advanced LLMs because they lack foundational skills and compositional examples in the same prompt context. |
| Approach: | They propose to use compositional generalization capabilities in large language models to elicit compositional skills in a prompt context. |
| Outcome: | The proposed structure enables LLMs to tackle more challenging problems with as few as two exemplars and unlocks their latent potential. |
Mitigating Biases in Hate Speech Detection from A Causal Perspective (2023.findings-emnlp)
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| Challenge: | Existing methods to detect hate speech are prone to spurious correlations between training data and labels, which could lead to biased treatment of vulnerable and minority groups. |
| Approach: | They propose to use grammar induction to find grammar patterns for hate speech and analyze this phenomenon from a causal perspective. |
| Outcome: | The proposed methods can detect hate speech from a causal perspective and are robust to different datasets. |
From Tasks to Teams: A Risk-First Evaluation Framework for Multi-Agent LLM Systems in Finance (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on task specific metrics such as accuracy, F1 score, or ROUGE. |
| Approach: | They propose a multi-agent, safety-aware evaluation agent that audits large language models without fine-tuning. |
| Outcome: | M-SAEA identifies unsafe trajectories with minimal false positives and reveals latent risks that are not addressed by standard metrics. |
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2023.tacl-1)
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| Challenge: | Existing methods for enhancing data efficiency in limited labeled data are limited. |
| Approach: | They propose to use data augmentation methods to increase the efficiency of limited data learning in NLP. |
| Outcome: | The proposed methods perform well on topics/news classification, inference tasks, paraphrasing tasks, and single-sentence tasks. |
Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (N19-1)
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| Challenge: | Existing models can't quantify persuasiveness of requests or extract successful persuasive strategies. |
| Approach: | They propose a semi-supervised hierarchical neural network model to quantify persuasiveness and identify persuasive strategies in advocacy requests. |
| Outcome: | The proposed method outperforms baseline models and offers increased interpretability of persuasive speech. |
Human-in-the-loop Abstractive Dialogue Summarization (2023.findings-acl)
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| Challenge: | Abstractive dialogue summarization systems are trained to maximize the likelihood of human-written summaries, but there is still a huge gap in generating high-quality summary as determined by humans. |
| Approach: | They propose to incorporate different levels of human feedback into the training process . they ask humans to highlight salient information to be included in summaries . |
| Outcome: | The proposed model captures human-written summaries and compares them with state-of-the-art models on multiple datasets. |
Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization (2020.emnlp-main)
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| Challenge: | Existing studies on text summarization focus on single-speaker docs, scientific publications and encyclopedia articles. |
| Approach: | They propose a multi-view sequence-to-sequence model that extracts conversational structures from unstructured daily chats and incorporates different views to generate dialogue summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models via automatic evaluation and human judgment on a large-scale dialogue summarization corpus. |
Examining the Ordering of Rhetorical Strategies in Persuasive Requests (2020.findings-emnlp)
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| Challenge: | Numerous studies have been conducted to understand persuasiveness of text, from explorations of rhetoric in presidential campaigns to the impact of a communicator's likability on persuasiveness. |
| Approach: | They use a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus and visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual request. |
| Outcome: | The proposed model disentangles content and rhetorical strategies in textual requests from a large-scale loan request corpus and visualizes interplay between content and strategy through attentional LSTM that predicts the success rate of textual request. |