How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective

summary

Video file (mp4)

The gist

The gist The reasoning and planning abilities of Large Language Models (LLMs) are limited because their outputs often contain mistaken or hallucinated actions, making them unreliable as planners.

In short

The paper investigates whether Large Language Models (LLMs) can reliably act as planners by treating planning as a Natural Language Processing (NLP) task. The authors propose a pipeline that evaluates, recovers, and completes flawed LLM plans using NLP and symbolic planners. Findings show LLMs lack underlying reasoning, with only about 2.65% of generated actions being executable, indicating they are far from the reliability of classical symbolic planners.

Key concepts

LLMs in Planning
Large Language Models are used to generate plans from unstructured input like natural language. The paper treats this as an NLP task because LLMs are fundamentally NLP models. However, they struggle with logical constraints and action effects, making them unreliable for complex planning.
Recovery Pipeline
This is a multi-step process designed to fix flawed LLM plans. It involves three stages: first, using NLP metrics to assess plan quality; second, manipulating the generated text through intermediate plans ($\pi_i$); and finally, using a symbolic planner to generate a valid final plan ($\pi_4$).
Plan Quality Metrics
Instead of just checking if a plan succeeds (success rate), the authors use NLP metrics like LCS (Longest Common Subsequence) to compare an LLM's plan ($\pi_0$) against a correct ground-truth plan ($\pi_{GT}$). This allows for finer evaluation, labeling actions as 'correct,' 'misplaced,' or 'redundant.'
Symbolic Planning
This refers to the traditional method of planning that uses formal logic and structured descriptions (like PDDL) to ensure plans are logically sound. The paper uses a symbolic planner as the final step in its recovery pipeline because it provides the necessary logical consistency that LLMs lack.

Terminology used across episodes

This episode discusses

The paper

How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective · Read on arXiv

Ma’ayan Armony, Albert Merono-Peñnuela, Gerard Canal

King’s College London

AI planning provides agents the ability to act autonomously towards achieving a goal in a given environment. Recent advances in Large Language Models (LLMs) have introduced the prospect of using unstructured input for planning, making LLMs in planning an active area of research, especially for supporting autonomous agents. However, LLMs remain unreliable as long-horizon planners, since some of the plans they produce contain mistaken or hallucinated actions; executing such plans could lead to unexpected and harmful outcomes. Existing benchmarks for LLMs in planning primarily rely on binary evaluation metrics, providing limited insight into the quality of plans that do not achieve the goal, and how far those are from becoming valid. In this paper, we frame the evaluation of LLMs in planning as a natural language processing (NLP) task, as LLMs are themselves NLP models. We propose a pipeline comprising three NLP-based steps that assess plan quality from complementary perspectives, followed by a symbolic planning step to achieve a valid plan, to provide a finer-grained understanding of the quality of invalid plans. In our experiments, we find no clear evidence of logical derivation or state-tracking during LLM plan generation -- on average, for all non-empty plans, only 3.55 actions per plan are executable, despite symbolically-generated optimal plans having an average length of 9.47 actions, which suggests that LLMs are still far from the reliability of symbolic planners. Our suite of metrics is further used to investigate patterns in LLM plan generation over time, and can provide a useful metric for assessing the quality of individual invalid plans.

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective".

Jane: Here's the gist of the paper. The gist The reasoning and planning abilities of Large Language Models (LLMs) are limited because their outputs often contain mistaken or hallucinated actions, making them unreliable as planners.

Tom: How it works The paper approaches planning with LLMs as a natural language processing (NLP) task, treating the plan as a form of language output given that LLMs are NLP models themselves.

Jane: The authors propose a recovery pipeline consisting of an NLP-based evaluation of the generated plans, along with three stages to recover the plans through NLP manipulation of the LLM-generated plans, and eventually complete the plan using a symbolic planner This pipeline provides a holistic analysis of LLM capabilities in the context of AI task planning, enabling a broader understanding of the quality of invalid plans.

Tom: Plan Evaluation and Validation The proposed evaluation pipeline includes NLP-based metrics to evaluate the quality of plans, which better capture the nature of LLM-generated plans.

Paper summary: Tom: So, to recap, the paper "How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective" frames planning with LLMs as an NLP task and proposes a recovery pipeline to assess plan quality beyond just whether it hits the goal.

Jane: They focus on comparing the generated plans, called pi zero to a ground-truth plan, pi GT, and they use NLP-based metrics to assign quality labels like "one correct" or "five redundant".

Lu: They also talk about using Longest Common Subset and Longest Common Subsequence algorithms to quantify how similar the generated plan is structurally to the valid one.

Meng: That sounds like a lot of text processing overhead for something that should be simple, but they are trying to see the linguistic patterns in their errors.

Lalam: The paper claims this pipeline improves the success rate from twenty-one point nine percent to twenty-seven point five percent, which is an improvement of twenty-five percent for the successfully generated plans, and they also found that action quality doesn't get worse during generation, which is interesting because you'd think it should <ref:2508.01300#pg1>.

Tom: Wait, so they are getting a better success rate, but are those plans actually good enough to be trusted when we deploy them?

Jane: The authors argue that their pipeline provides novel insights into the potential of plans and helps us measure exactly where and how LLMs go wrong in generating plans, which is important for error analysis.

Lu: And they explicitly argue that LLMs fail at showing NLP-based evidence of reasoning required in planning algorithms, making them unreliable as planners.

Tom: That's a big statement then. So even with the recovery steps, the core issue seems to be a lack of underlying logical derivation during plan generation itself.

Meng: From my side, if it can't show that reasoning during generation, it means we can't really rely on the LLM to do the hard thinking part of planning alone.

Jane: The numbers they give are telling us that on average, for all non-empty plans, only three point five five actions per plan are executable, which is way less than the symbolically generated optimal plans that have an average length of nine point four seven actions.

Lalam: And their study also shows that NL oneshot performs best by most metrics even when its PDDL counterpart has a higher initial success rate, and the recovery pipeline is more effective for shorter ground-truth plans, meaning it really helps when the target plan is already relatively short.

Conclusion: Tom: So to wrap up this discussion on "How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective", the main message is that success rate alone isn't a reliable measure for plan quality.

Jane: The paper shows that planning cannot be reliably done based solely on LLM reasoning; you still need a classical planner to improve the plan after the initial generation.

Lu: The authors conclude that their pipeline, even with all the recovery and refinement steps, still falls short of the quality and reliability of classical planners.

Meng: So what does this mean for deployment? It suggests that while LLMs can generate text that looks like a plan, they lack the core reasoning to ensure those actions are logically sound in a complex environment.

Lalam: The paper also points out that the quality of LLM plan generation might actually depend on the specific architecture of the models they are using.

Tom: It sounds like future work needs to focus more on evaluating how these models handle temporal constraints, which is something they mention as an area for future investigation.

Jane: Exactly. We need to adapt these evaluation components to properly test the LLMs' temporal ability in planning, because right now, relying only on their linguistic output isn't enough for safe autonomous action.

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