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Found 6 papers, 2 papers with code
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
The study seems to indicate that (i) LLMs are bad at solving graph coloring instances (ii) they are no better at verifying a solution--and thus are not effective in iterative modes with LLMs critiquing LLM-generated solutions (iii) the correctness and content of the criticisms--whether by LLMs or external solvers--seems largely irrelevant to the performance of iterative prompting.
Can Large Language Models Really Improve by Self-critiquing Their Own Plans?
We evaluate a planning system that employs LLMs for both plan generation and verification.
On the Planning Abilities of Large Language Models : A Critical Investigation
We aim to evaluate (1) the effectiveness of LLMs in generating plans autonomously in commonsense planning tasks and (2) the potential of LLMs in LLM-Modulo settings where they act as a source of heuristic guidance for external planners and verifiers.
On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)
On this benchmark, we evaluate LLMs in three modes: autonomous, heuristic and human-in-the-loop.
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
If the relevant concept is not in the shared vocabulary, then it is learned.
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change
PlanBench provides sufficient diversity in both the task domains and the specific planning capabilities.
