325 citations · 363 across the 15 of their papers we have counts for
16 papers
X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains
Qianchu Liu, Sheng Zhang, Guanghui Qin +9
Recent proprietary models (e.g., o3) have begun to demonstrate strong multimodal reasoning capabilities. Yet, most existing open-source research concentrates on training text-only…
KV-Distill: Nearly Lossless Learnable Context Compression for LLMs
Vivek Chari, Guanghui Qin, Benjamin Van Durme
Sequence-to-sequence tasks often benefit from long contexts, but the quadratic complexity of self-attention in standard Transformers renders this non-trivial. During generation, te…
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning
Sheng Zhang, Qianchu Liu, Guanghui Qin +2
Reinforcement learning from verifiable rewards (RLVR) has recently gained attention for its ability to elicit self-evolved reasoning capabilitie from base language models without e…
CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
Abe Bohan Hou, Orion Weller, Guanghui Qin +5
Legal professionals need to write analyses that rely on citations to relevant precedents, i.e., previous case decisions. Intelligent systems assisting legal professionals in writin…
Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents
Corby Rosset, Ho-Lam Chung, Guanghui Qin +5
Existing question answering (QA) datasets are no longer challenging to most powerful Large Language Models (LLMs). Traditional QA benchmarks like TriviaQA, NaturalQuestions, ELI5 a…
Streaming Sequence Transduction through Dynamic Compression
Weiting Tan, Yunmo Chen, Tongfei Chen +5
We introduce STAR (Stream Transduction with Anchor Representations), a novel Transformer-based model designed for efficient sequence-to-sequence transduction over streams. STAR dyn…
Nugget: Neural Agglomerative Embeddings of Text
Guanghui Qin, Benjamin Van Durme
Embedding text sequences is a widespread requirement in modern language understanding. Existing approaches focus largely on constant-size representations. This is problematic, as t…
Dodo: Dynamic Contextual Compression for Decoder-only LMs
Guanghui Qin, Corby Rosset, Ethan C. Chau +2
Transformer-based language models (LMs) are inefficient in long contexts. We propose Dodo, a solution for context compression. Instead of one vector per token in a standard transfo…
The NLP Task Effectiveness of Long-Range Transformers
Guanghui Qin, Yukun Feng, Benjamin Van Durme
Transformer models cannot easily scale to long sequences due to their O(N^2) time and space complexity. This has led to Transformer variants seeking to lower computational complexi…
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Mahsa Yarmohammadi, Shijie Wu, Marc Marone +10
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other languag…
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
Guanghui Qin, Jason Eisner
Natural-language prompts have recently been used to coax pretrained language models into performing other AI tasks, using a fill-in-the-blank paradigm (Petroni et al., 2019) or a f…
LOME: Large Ontology Multilingual Extraction
Patrick Xia, Guanghui Qin, Siddharth Vashishtha +7
We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions…
CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models
Abhinav Singh, Patrick Xia, Guanghui Qin +2
Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output. These frameworks, operating at the lexical ty…
Iterative Paraphrastic Augmentation with Discriminative Span Alignment
Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin +2
We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment. Our approach allows for the la…
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Hongyuan Mei, Guanghui Qin, Minjie Xu +1
Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large. Training an unrestricted neural model might overfit t…
Imputing Missing Events in Continuous-Time Event Streams
Hongyuan Mei, Guanghui Qin, Jason Eisner
Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose parti…