6 papers · 1 filter
Positional Biases Shift as Inputs Approach Context Window Limits
Blerta Veseli, Julian Chibane, Mariya Toneva +1
Large Language Models (LLMs) often struggle to use information across long inputs effectively. Prior work has identified positional biases, such as the Lost in the Middle (LiM) eff…
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…
Scope-enhanced Compositional Semantic Parsing for DRT
Xiulin Yang, Jonas Groschwitz, Alexander Koller +1
Discourse Representation Theory (DRT) distinguishes itself from other semantic representation frameworks by its ability to model complex semantic and discourse phenomena through st…
Fine-grained Controllable Text Generation through In-context Learning with Feedback
Sarubi Thillainathan, Alexander Koller
We present a method for rewriting an input sentence to match specific values of nontrivial linguistic features, such as dependency depth. In contrast to earlier work, our method us…
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…