3 papers
cs.CL2025
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…
cs.AI2025
Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
Katharina Stein, Nils Hodel, Daniel Fišer +3
LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previ…
cs.CL2025
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…