2 citations · 4 across the 13 of their papers we have counts for
13 papers
Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?
Ayan Antik Khan, Harsh Kohli, Yuekun Yao +2
Mechanistic interpretability has made substantial progress in automatically localizing circuits, but explaining what localized components do remains labor-intensive and difficult t…
QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
Jian Xie, Tianhe Lin, Zilu Wang +16
Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information.…
Barriers to Universal Reasoning With Transformers (And How to Overcome Them)
Oliver Kraus, Yash Sarrof, Yuekun Yao +2
Chain-of-Thought (CoT) has been shown to empirically improve Transformers' performance, and theoretically increase their expressivity to Turing completeness. However, whether Trans…
Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers
Harsh Kohli, Srinivasan Parthasarathy, Huan Sun +1
We study implicit reasoning, i.e. the ability to combine knowledge or rules within a single forward pass. While transformer-based large language models store substantial factual kn…
Reason to Rote: Rethinking Memorization in Reasoning
Yupei Du, Philipp Mondorf, Silvia Casola +3
Large language models readily memorize arbitrary training instances, such as label noise, yet they perform strikingly well on reasoning tasks. In this work, we investigate how lang…
Language models can learn implicit multi-hop reasoning, but only if they have lots of training data
Yuekun Yao, Yupei Du, Dawei Zhu +2
Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass, without chain of thought. We investigate this capability using GP…
Anything Goes? A Crosslinguistic Study of (Im)possible Language Learning in LMs
Xiulin Yang, Tatsuya Aoyama, Yuekun Yao +1
Do language models (LMs) offer insights into human language learning? A common argument against this idea is that because their architecture and training paradigm are so vastly dif…
Predicting generalization performance with correctness discriminators
Yuekun Yao, Alexander Koller
The ability to predict an NLP model's accuracy on unseen, potentially out-of-distribution data is a prerequisite for trustworthiness. We present a novel model that establishes uppe…
SLOG: A Structural Generalization Benchmark for Semantic Parsing
Bingzhi Li, Lucia Donatelli, Alexander Koller +3
The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions. Existing benchmarks often focus on lexical gene…
Structural generalization is hard for sequence-to-sequence models
Yuekun Yao, Alexander Koller
Sequence-to-sequence (seq2seq) models have been successful across many NLP tasks, including ones that require predicting linguistic structure. However, recent work on compositional…
Compositional Generalization Requires Compositional Parsers
Pia Weißenhorn, Yuekun Yao, Lucia Donatelli +1
A rapidly growing body of research on compositional generalization investigates the ability of a semantic parser to dynamically recombine linguistic elements seen in training into…
ELITR Non-Native Speech Translation at IWSLT 2020
Dominik Macháček, Jonáš Kratochvíl, Sangeet Sagar +6
This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to…
Dynamic Masking for Improved Stability in Spoken Language Translation
Yuekun Yao, Barry Haddow
For spoken language translation (SLT) in live scenarios such as conferences, lectures and meetings, it is desirable to show the translation to the user as quickly as possible, avoi…