7 citations · 13 across the 10 of their papers we have counts for
11 papers
Joint Subspace Sparse Attention for Efficient Long-Context Decoding
Jiwon Song, Yoongon Kim, Jae-Joon Kim
Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely operate at coarse granularity. Token-level selection offers fi…
RelayGen: Intra-Generation Model Switching for Efficient Reasoning
Jiwon Song, Yoongon Kim, Jae-Joon Kim
Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks by generating long, multi-step reasoning trajectories, but inference-time scaling incurs substan…
DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
Rulin Shao, Akari Asai, Shannon Zejiang Shen +18
Deep research agents perform multi-step research to produce long-form, well-attributed answers. However, most open deep research agents are trained on easily verifiable short-form…
Learning to Interpret Weight Differences in Language Models
Avichal Goel, Yoon Kim, Nir Shavit +1
Finetuning (pretrained) language models is a standard approach for updating their internal parametric knowledge and specializing them to new tasks and domains. However, the corresp…
On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts
Linlu Qiu, Cedegao E. Zhang, Joshua B. Tenenbaum +2
Language use is shaped by pragmatics -- i.e., reasoning about communicative goals and norms in context. As language models (LMs) are increasingly used as conversational agents, it…
Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models
Linlu Qiu, Fei Sha, Kelsey Allen +3
Large language models (LLMs) are increasingly used as agents that interact with users and with the world. To do so successfully, LLMs must construct representations of the world an…
LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory
Jillian Ross, Yoon Kim, Andrew W. Lo
Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us t…
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps
Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh +3
When asked to summarize articles or answer questions given a passage, large language models (LLMs) can hallucinate details and respond with unsubstantiated answers that are inaccur…
MDCR: A Dataset for Multi-Document Conditional Reasoning
Peter Baile Chen, Yi Zhang, Chunwei Liu +3
The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a schola…
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models
Yue Zhou, Yada Zhu, Diego Antognini +2
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models. We find that subtle alterations in the surface…
CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities
Yujun Mao, Yoon Kim, Yilun Zhou
Recent large language models (LLMs) have shown indications of mathematical reasoning ability on challenging competition-level problems, especially with self-generated verbalization…