8 citations · 12 across the 10 of their papers we have counts for
14 papers
FinRegQA-EU: Corruption-Based Preference Data for Grounded EU Financial Regulatory Question Answering
Aulia Kharis Rakhmasari, Fan Yu, Alexander Hoyle +1
Large Language Models (LLMs) struggle with region-specific factual knowledge, particularly in financial regulation. While benchmarks such as CFinBench, provide broad coverage of fi…
Doppelganger: Sound Effects and Their Synthetic Twins
Elliott Ash
Audio-conditioned generators now produce synthetic sound effects from real recordings, so the real and synthetic versions of an event increasingly coexist in sound libraries and in…
Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification
Chenfei Xiong, Jingwei Ni, Yu Fan +10
We introduce Co-DETECT (Collaborative Discovery of Edge cases in TExt ClassificaTion), a novel mixed-initiative annotation framework that integrates human expertise with automatic…
The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure
Yu Fan, Yang Tian, Shauli Ravfogel +3
Embedding-based similarity metrics between text sequences can be influenced not just by the content dimensions we most care about, but can also be biased by spurious attributes lik…
Can Reasoning Help Large Language Models Capture Human Annotator Disagreement?
Jingwei Ni, Yu Fan, Vilém Zouhar +6
Variation in human annotation (i.e., disagreements) is common in NLP, often reflecting important information like task subjectivity and sample ambiguity. Modeling this variation is…
LEXam: Benchmarking Legal Reasoning on 340 Law Exams
Yu Fan, Jingwei Ni, Jakob Merane +14
Long-form legal reasoning remains a key challenge for large language models (LLMs) in spite of recent advances in test-time scaling. To address this, we introduce LEXam, a novel be…
Balancing Truthfulness and Informativeness with Uncertainty-Aware Instruction Fine-Tuning
Tianyi Wu, Jingwei Ni, Bryan Hooi +5
Instruction fine-tuning (IFT) can increase the informativeness of large language models (LLMs), but may reduce their truthfulness. This trade-off arises because IFT steers LLMs to…
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation
Jingwei Ni, Tobias Schimanski, Meihong Lin +3
Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents. But do RAG implementations leave out important information when…
Aligning Large Language Models with Diverse Political Viewpoints
Dominik Stammbach, Philine Widmer, Eunjung Cho +2
Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we a…
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
Jingwei Ni, Minjing Shi, Dominik Stammbach +3
With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important. However, factual claim detection, the first step in a…
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
Tobias Schimanski, Jingwei Ni, Mathias Kraus +2
Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is b…
LePaRD: A Large-Scale Dataset of Judges Citing Precedents
Robert Mahari, Dominik Stammbach, Elliott Ash +1
We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate wor…
Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers
Elliott Ash, Aniket Kesari, Suresh Naidu +2
Judicial opinions are written to be persuasive and could build public trust in court decisions, yet they can be difficult for non-experts to understand. We present a pipeline for u…
The Law and NLP: Bridging Disciplinary Disconnects
Robert Mahari, Dominik Stammbach, Elliott Ash +1
Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the…