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AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn

Difei Gao, Lei Ji, Luowei Zhou, Kevin Qinghong Lin, Joya Chen, Zihan Fan, Mike Zheng Shou

arXiv:2306.08640v2cs.CV

TL;DR

Complex visual tasks can require plans based on visual content and intermediate results, not just the query. AssistGPT uses interleaved language-and-code reasoning with tools to address these tasks. The paper reports effectiveness on A-OKVQA and NExT-QA and showcases complex real-world scenarios.

  • Problem

    Complex visual tasks can require diverse reasoning paths based on visual content and multimodal intermediate results, which are challenging to manage in general assistant systems.

    Source: §1 · pp. 1–2

  • Method

    AssistGPT interleaves language planning with structured tool calls and uses an Inspector to manage visual inputs and results and a Learner to assess and record successful trials.

    Source: §1 · p. 3, §3 · p. 4

  • Results

    AssistGPT demonstrates effectiveness on A-OKVQA and NExT-QA; it achieves the best results among current methods on the A-OKVQA multi-choice in-context learning setting.

    Source: §4.2 · p. 8, §5 · p. 10

  • Takeaways & Limitations

    The paper showcases AssistGPT handling diverse real-world scenarios and questions more complex than those in the benchmarks.

    Source: §1 · p. 3, §5 · p. 10, §4.3 · p. 10

    AssistGPT did not surpass previous methods on direct-answer questions, where verbose answer phrasing can cause otherwise correct responses to be marked incorrect.

    Source: §4.2 · p. 8

Abstract

from arXiv · show

Recent research on Large Language Models (LLMs) has led to remarkable advancements in general NLP AI assistants. Some studies have further explored the use of LLMs for planning and invoking models or APIs to address more general multi-modal user queries. Despite this progress, complex visual-based tasks still remain challenging due to the diverse nature of visual tasks. This diversity is reflected in two aspects: 1) Reasoning paths. For many real-life applications, it is hard to accurately decompose a query simply by examining the query itself. Planning based on the specific visual content and the results of each step is usually required. 2) Flexible inputs and intermediate results. Input forms could be flexible for in-the-wild cases, and involves not only a single image or video but a mixture of videos and images, e.g., a user-view image with some reference videos. Besides, a complex reasoning process will also generate diverse multimodal intermediate results, e.g., video narrations, segmented video clips, etc. To address such general cases, we propose a multi-modal AI assistant, AssistGPT, with an interleaved code and language reasoning approach called Plan, Execute, Inspect, and Learn (PEIL) to integrate LLMs with various tools. Specifically, the Planner is capable of using natural language to plan which tool in Executor should do next based on the current reasoning progress. Inspector is an efficient memory manager to assist the Planner to feed proper visual information into a specific tool. Finally, since the entire reasoning process is complex and flexible, a Learner is designed to enable the model to autonomously explore and discover the optimal solution. We conducted experiments on A-OKVQA and NExT-QA benchmarks, achieving state-of-the-art results. Moreover, showcases demonstrate the ability of our system to handle questions far more complex than those found in the benchmarks.

1 Introduction

The introduction argues that complex visual tasks require reasoning paths informed by visual content and intermediate results, which existing approaches can struggle to manage. AssistGPT combines language planning, structured tool calls, visual-input management, and learning from successful trials.

1 Introduction

  • Complex high-level queries may require different reasoning paths depending on whether relevant information appears in subtitles, actions, video text, or combinations of these.The introduction says a reason-only approach can struggle to self-correct after a mistake.Source: §1 · p. 2, Figure 2 · p. 3
  • Multimodal tool outputs such as cropped image regions and segmented video clips are difficult for a Planner to select and route to subsequent modules.The paper contrasts these results with NLP tool outputs that can be passed directly to later models.Source: §1 · pp. 2–3
  • AssistGPT combines language planning and structured tool calls with an Inspector for visual inputs and intermediate results, and a Learner that records successful trials.The Planner chooses next steps from reasoning progress, while the system also supports repeated attempts.Source: Figure 1 · p. 2, §1 · p. 3
  • AssistGPT is presented as a multimodal assistant that combines multiple models for diverse visual tasks, including complex images, long-form videos, high-level queries, and flexible inputs.The introduction notes that text extracted from complex visual scenes may include irrelevant information or omit query-relevant details.Source: §1 · pp. 2–3

2 Related Work

The related work describes task-specific multimodal systems and two broad strategies for general multimodal assistance: directly processing visual inputs or composing external tools. It positions AssistGPT's interleaved language-and-code reasoning and retry-based learning among these approaches.

2 Related Work

  • Earlier multimodal models target specific visual, knowledge, action, video, or text tasks, while the paper describes their generalizability as limited for complex real-world questions.The work also notes that directly processing multimodal inputs can leave advanced grounding and audio needs unmet, with high scaling costs.Source: §2 · pp. 3–4
  • General multimodal systems either condition LLMs on visual features or combine models and APIs, and AssistGPT follows the latter strategy with PEIL.The cited examples include GPT-4, PaLM-E, BLIP-2, Mini-GPT4, Socratic, Visual ChatGPT, HuggingGPT, and ViperGPT.Source: §2 · p. 4
  • Compositional visual question answering offers interpretable module responsibilities, but traditional methods often require ground-truth decomposition or reinforcement learning for module use.The related work traces the approach to [57] and LSTM/CNN variants to [58] [59].Source: §2 · p. 4
  • Prior LLM-based planners use language reasoning or code, while AssistGPT proposes interleaving both and using visual inputs or execution feedback to choose subsequent steps for complex queries.The section also notes that MMReAct had not demonstrated benchmark effectiveness at the time described.Source: §2 · p. 4
  • AssistGPT's learning scheme retries, then preserves successful explorations as in-context examples rather than fine-tuning the model as in Toolformer.The section contrasts this with early modular reinforcement learning, which can be costly when modules are LLMs.Source: §2 · p. 4

3 AssistGPT

AssistGPT coordinates a Planner, Executor, Inspector, and Learner to answer queries using reference images or videos. Their roles cover planning and tool feedback, input and intermediate-result management, and assessment of system performance.

3 AssistGPT

  • AssistGPT uses four cooperating modules: the Planner controls reasoning, the Executor runs tools, the Inspector manages inputs and intermediate results, and the Learner assesses performance and records successful explorations.The system accepts a general language query with reference images or videos.Source: §3 · p. 4, Figure 3 · p. 5

3.1 Planner

  • The Planner uses GPT-4 and an instruction prompt, query, and Inspector summary to produce a natural-language Thought and a structured Action specifying a tool and its arguments.Executor observations and Inspector summaries can inform the next planning step.Source: §3.1 · p. 5
  • AssistGPT's tools are grouped as Descriptors, Locators, and Reasoners, spanning perception, information grounding, and reasoning over extracted information.The listed modules include image captioning, video narration, object detection, text detection, ASR translation, grounding, and temporal reasoning.Source: §3.1 · pp. 5–6

3.2 Executor

  • The Executor validates Planner code, runs the selected module through a unified interface, and translates results into language observations for the Planner.Invalid code produces an error message that lets the Planner revise its plan rather than halting the reasoning process.Source: §3.2 · p. 6

3.3 Inspector

  • The Inspector tracks visual-element metadata, including type, source, and description, and adds summaries of new inputs and outputs to the Planner's reasoning history.Video metadata can also include duration and whether audio or subtitles are present.Source: §3.3 · pp. 6–7

3.4 Learner

  • The Learner evaluates reasoning by self-assessment or ground-truth comparison, retries unsatisfactory answers, and saves successful revised plans as in-context examples.If attempts still fail, the paper identifies module or API updates as a possible future need.Source: §3.4 · p. 7

4 Experiments

On A-OKVQA and NExT-QA, AssistGPT is evaluated against prior methods and through component ablations; qualitative examples illustrate its stepwise reasoning and self-correction.

4.1 Experimental Setting

  • The evaluation uses the A-OKVQA and NExT-QA validation sets to assess visual question answering and video reasoning, respectively.A-OKVQA validation has 1,145 questions; NExT-QA validation has 4,996.Source: §4.1 · p. 7

4.2 Quantitative Results

  • Reason-only planning improves direct-answer and multi-choice performance, while ReAct improves both further and interleaved language-and-code reasoning brings a larger multi-choice gain.The PEIL variants with Self-Check and GT-Check score highest on both tracks; the Learner particularly improves direct answers by collecting in-context examples to address overly long answers.Source: §4.2 · pp. 8–9, Table 3 · p. 8

4.3 Qualitative Results

  • In the qualitative examples, AssistGPT decomposes questions into subtasks, invokes needed content, and can revise its plan after an unsatisfactory result fails self-check.The reason-only baseline cannot self-optimize when its caption-based prediction is incorrect; the paper also presents a more complex example beyond benchmark questions.Source: §4.3 · pp. 9–10, Figure 4 · p. 9

5 Conclusions and Limitations

AssistGPT uses interleaved code and language reasoning, called Plan, Execute, Inspect, and Learn (PEIL), to integrate an LLM with tools for complex visual tasks. The paper reports effectiveness on A-OKVQA and NExT-QA and showcases intricate real-world scenarios, while noting limits in tool updating and planning overhead.

5 Conclusions and Limitations

  • AssistGPT uses interleaved code and language reasoning, called Plan, Execute, Inspect, and Learn (PEIL), to integrate an LLM with tools for complex visual tasks.The conclusion names the system and its core approach.Source: §5 · p. 10
  • Experiments on A-OKVQA and NExT-QA demonstrate AssistGPT’s effectiveness, and showcases cover diverse, intricate real-world scenarios.Source: §5 · p. 10
  • The approach does not provide end-to-end updating when tools make mistakes, and its planning requires extensive tool explanations, creating overhead.The paper suggests distilling a smaller planner as a possible way to improve the overhead.Source: §5 · p. 10

Appendix

The appendix supplements the main paper with discussion of modular systems, implementation details, qualitative A-OKVQA results, and in-the-wild examples.

Appendix

  • The appendix adds discussion comparing AssistGPT with existing modular systems.Source: Appendix · p. 10
  • It provides further details about AssistGPT’s implementation and qualitative A-OKVQA results.Source: Appendix · p. 10
  • It also includes additional in-the-wild examples.Source: Appendix · p. 10

A Discussion with Existing LLM-driven Modular Systems

The discussion compares modular systems across task focus, reasoning, source management, and learning capabilities. It argues that PEIL’s interleaved language-and-code reasoning, Inspector, and update mechanism address challenges involving complex videos, flexible sources, and continued optimization.

Comparison Framework

  • Table 5 compares modular systems on task focus, reasoning method, source management, and learning capabilities; its “ReAct” label means concurrent planning and execution, not strictly the ReAct method.Source: Table 5 · p. 10

Task Focus

  • AssistGPT handles complex video question answering across subtitles, visual content, and OCR, with long-video comprehension; HuggingGPT and MM-ReAct show video capabilities through simple examples but lack benchmark validation.Source: A Discussion with Existing LLM-driven… · pp. 10–11

Reasoning

  • PEIL combines flexible language planning with structured code for invoking inputs and intermediate results, addressing complex video questions beyond reason-only planning and MM-ReAct’s basic two-step video scheme.Source: A Discussion with Existing LLM-driven… · p. 11

Source Management

Learning

B More details of AssistGPT

AssistGPT’s modules provide image and video captioning, detection, grounding, audio transcription, and reasoning over knowledge, narration, and subtitles. Several implementation choices are preliminary, including fixed video segmentation for temporal reasoning.

Image Caption

  • The Image Caption module uses a text-conditioned captioning model, such as BLIP2 or InstructBLIP, in a role similar to open-ended visual question answering.Source: B More details of AssistGPT · p. 11

Video Narration

  • Video Narration samples frames at 1/3 FPS and captions them with text conditioning; OCR can optionally be appended to each frame’s caption.Dense captioning is avoided because its output would be excessively abundant for subsequent models.Source: B More details of AssistGPT · p. 12

Detection

Audio and Subtitle Grounding

  • ASR Translation converts video audio to timestamped text and runs automatically when an audio-containing video arrives; Subtitle Ground uses video subtitles with GPT-4 to locate relevant segments.Source: B More details of AssistGPT · p. 12

Image Grounding

  • Region Ground finds image areas matching a query, while Text Ground locates text using OCR matches and can refine multiple matches by the named object.Source: B More details of AssistGPT · p. 12

Reasoning Modules

  • Knowledge Reason uses GPT-4’s learned knowledge with the question and prior reasoning results, without retrieving knowledge from the internet.Source: B More details of AssistGPT · p. 12
  • Narration Reason and Subtitle Reason infer answers from video narration and subtitles, respectively, using GPT-4 with the query.Source: B More details of AssistGPT · p. 12

Temporal Reason

  • Temporal Reason divides videos into five segments for absolute temporal terms and eight for relative terms, but these clip-division hyperparameters are preliminary.The paper identifies semantic video division as a future improvement.Source: B More details of AssistGPT · pp. 12–13

C Qualitative Results in A-OKVQA

AssistGPT produces interpretable answer processes, including reasonable explanations for some incorrect answers. The examples illustrate recurring errors in output formatting, fine-grained recognition, pose-to-text mapping, and consistency.

C Qualitative Results in A-OKVQA

  • Even incorrect answers can come with relatively reasonable explanations, and the examples illustrate frequent A-OKVQA error patterns.The paper describes the answer processes as highly interpretable.Source: C Qualitative Results in A-OKVQA · p. 13
  • Direct Answer responses can express the correct meaning but still count as incorrect when their wording differs from the expected answer.Source: C Qualitative Results in A-OKVQA · p. 13
  • Fine-grained object recognition errors, such as failing to recognize cough drops, can lead to incorrect final answers.Source: C Qualitative Results in A-OKVQA · p. 13
  • AssistGPT lacks a related model for mapping fine-grained poses or actions to language, so it may infer an action from surrounding objects instead.The example describes difficulty identifying a cat’s upward jump.Source: C Qualitative Results in A-OKVQA · p. 13
  • Reasoning can become inconsistent when an object label changes across steps, and the system may fail to detect and correct the inconsistency.One example shifts from identifying an orange vehicle as a truck to calling it a shuttle bus.Source: C Qualitative Results in A-OKVQA · p. 13

D In-the-wild Prediction Examples

The in-the-wild examples show AssistGPT handling varied video types and high-level queries, including cases with multiple video inputs. They also illustrate self-correction and multimodal answers, while the paper characterizes its real-world queries as more complex than benchmark samples.

D In-the-wild Prediction Examples

  • AssistGPT handles dense subtitled instructional videos and videos with rich visual content but sporadic on-frame text.Source: D In-the-wild Prediction Examples · p. 13
  • For high-level queries, examples show strategic content search, correct responses, comprehensive multimodal answers, and self-correction when narration fails and subtitles are used instead.Source: D In-the-wild Prediction Examples · p. 13
  • One example combines a user-view image with reference videos, and the paper says AssistGPT can accommodate any number of inputs.Adding YouTube search could let it seek reference videos and cross-reference them to discern user intent.Source: D In-the-wild Prediction Examples · p. 13
  • The paper characterizes AssistGPT as handling real-world application queries more complex and comprehensive than benchmark samples.Source: D In-the-wild Prediction Examples · p. 13
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