# PALM: Predicting Actions through Language Models

arXiv: https://arxiv.org/abs/2311.17944 · HTML: https://mlsanity.com/paper/2311.17944/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 69 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 30 of 54)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [GIT: A Generative Image-to-text Transformer for Vision and Language](https://mlsanity.com/paper/2205.14100/) (2022)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [Unified Vision-Language Pre-Training for Image Captioning and VQA](https://mlsanity.com/paper/1909.11059/) (2019)
- [InternVideo: General Video Foundation Models via Generative and Discriminative Learning](https://mlsanity.com/paper/2212.03191/) (2022)
- [Unifying Vision-and-Language Tasks via Text Generation](https://mlsanity.com/paper/2102.02779/) (2021)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)

## Built on by (most cited first, 29 of 29)

- [EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation](https://mlsanity.com/paper/2406.18070/) (2024)
- [Visual Large Language Models for Generalized and Specialized Applications](https://mlsanity.com/paper/2501.02765/) (2025)
- [Challenges and Trends in Egocentric Vision: A Survey](https://mlsanity.com/paper/2503.15275/) (2025)
- [TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos](https://mlsanity.com/paper/2411.02570/) (2024)
- [Multimodal Large Models Are Effective Action Anticipators](https://mlsanity.com/paper/2501.00795/) (2025)
- [Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation](https://mlsanity.com/paper/2508.01742/) (2025)
- [Technical Report for Ego4D Long-Term Action Anticipation Challenge 2025](https://mlsanity.com/paper/2506.02550/) (2025)
- [IMPACT: A Dataset for Multi-Granularity Human Procedural Action Understanding in Industrial Assembly](https://mlsanity.com/paper/2604.10409/) (2026)
- [Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance](https://mlsanity.com/paper/2606.04970/) (2026)
- [Benchmarking Vision, Language, & Action Models in Procedurally Generated, Open Ended Action Environments](https://mlsanity.com/paper/2505.05540/) (2025)
- [User-in-the-loop Evaluation of Multimodal LLMs for Activity Assistance](https://mlsanity.com/paper/2408.03160/) (2024)
- [ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction](https://mlsanity.com/paper/2504.01472/) (2025)
- [Vision and Intention Boost Large Language Model in Long-Term Action Anticipation](https://mlsanity.com/paper/2505.01713/) (2025)
- [Enhancing Visual Planning with Auxiliary Tasks and Multi-token Prediction](https://mlsanity.com/paper/2507.15130/) (2025)
- [Surgical Action Planning with Large Language Models](https://mlsanity.com/paper/2503.18296/) (2025)
- [Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights](https://mlsanity.com/paper/2504.00839/) (2025)
- [Leveraging Gaze and Set-of-Mark in VLLMs for Human-Object Interaction Anticipation from Egocentric Videos](https://mlsanity.com/paper/2604.03667/) (2026)
- [Rethinking Video Human-Object Interaction: Set Prediction over Time for Unified Detection and Anticipation](https://mlsanity.com/paper/2604.10397/) (2026)
- [SAP-Bench: Benchmarking Multimodal Large Language Models in Surgical Action Planning](https://mlsanity.com/paper/2506.07196/) (2025)
- [Human Centric General Physical Intelligence for Agile Manufacturing Automation](https://mlsanity.com/paper/2508.11960/) (2025)
- [EventFormer: A Node-graph Hierarchical Attention Transformer for Action-centric Video Event Prediction](https://mlsanity.com/paper/2510.21786/) (2025)
- [Seeing is Believing (and Predicting): Context-Aware Multi-Human Behavior Prediction with Vision Language Models](https://mlsanity.com/paper/2512.15957/) (2025)
- [MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents](https://mlsanity.com/paper/2601.20831/) (2026)
- [LAP: A Language-Aware Planning Model For Procedure Planning In Instructional Videos](https://mlsanity.com/paper/2603.09743/) (2026)
- [FactCheck: Feasibility-aware Long-term Action Anticipation with Multi-agent Collaboration](https://mlsanity.com/paper/2606.14778/) (2026)
- [Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior](https://mlsanity.com/paper/2609.17064/) (2026)
- [HINT-Plan: Human Intention-Aware Robot Task Planning in Context-Rich Environments using Vision Language Models](https://mlsanity.com/paper/2609.17771/) (2026)
- [Language-Augmented Video Action Anticipation: Design Fundamentals, Benchmarks, and Open Challenges](https://mlsanity.com/paper/2609.31665/) (2026)
- [NextMe-800: Anticipating Personal Behavior from Months of Egocentric Video](https://mlsanity.com/paper/2610.01461/) (2026)

## Abstract (arXiv)

Understanding human activity is a crucial yet intricate task in egocentric vision, a field that focuses on capturing visual perspectives from the camera wearer's viewpoint. Traditional methods heavily rely on representation learning that is trained on a large amount of video data. However, a major challenge arises from the difficulty of obtaining effective video representation. This difficulty stems from the complex and variable nature of human activities, which contrasts with the limited availability of data. In this study, we introduce PALM, an approach that tackles the task of long-term action anticipation, which aims to forecast forthcoming sequences of actions over an extended period. Our method PALM incorporates an action recognition model to track previous action sequences and a vision-language model to articulate relevant environmental details. By leveraging the context provided by these past events, we devise a prompting strategy for action anticipation using large language models (LLMs). Moreover, we implement maximal marginal relevance for example selection to facilitate in-context learning of the LLMs. Our experimental results demonstrate that PALM surpasses the state-of-the-art methods in the task of long-term action anticipation on the Ego4D benchmark. We further validate PALM on two additional benchmarks, affirming its capacity for generalization across intricate activities with different sets of taxonomies.