How Much Do Large Language Models Know about Human Motion? A Case Study in 3D Avatar Control (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a new study explores the human motion knowledge of Large Language Models (LLMs) using 3D avatar control. |
| Approach: | They use 20 representative motion instructions to interpolate LLMs into avatar animations . they find they are strong at interpreting high-level body movements but struggle with precise body part positioning . |
| Outcome: | The proposed model is strong at interpreting high-level body movements but struggles with precise body part positioning. |
Similar Papers
Can LLMs Truly Plan? A Comprehensive Evaluation of Planning Capabilities (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing assessments of planning capabilities of large language models are limited to single-language or specific representation formats. |
| Approach: | a new benchmark is developed to assess the planning capabilities of large language models. |
| Outcome: | The Multi-Plan benchmark highlights performance disparities among models . language differences showed minimal impact, while mathematically structured representations improved accuracy . |
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces (2025.acl-long)
Copied to clipboard
Baining Zhao, Jianjie Fang, Zichao Dai, Ziyou Wang, Jirong Zha, Weichen Zhang, Chen Gao, Yue Wang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored. |
| Approach: | They propose a benchmark to evaluate whether large multimodal models can process continuous first-person visual observations like humans. |
| Outcome: | The proposed model can process first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. |
Human Alignment: How Much Do We Adapt to LLMs? (2025.acl-short)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming a common part of our lives, yet few studies have examined how they influence our behavior. |
| Approach: | They propose a cooperative language game in which players aim to converge on a word and play a game in a group. |
| Outcome: | The proposed game shows that humans notice and adapt to differences regardless of whether they are aware they are interacting with an LLM. |
LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
| Approach: | They propose to integrate large language models into AP and NLP planning frameworks by reviewing current research and identifying critical challenges and future directions. |
| Outcome: | The proposed frameworks are used to support reliable off-the-shelf AP planners. |
Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives (2026.acl-long)
Copied to clipboard
| Challenge: | a new study examines whether large language models acquire embodied cognition and cultural conventions from training data . demonstratives are a natural lens for evaluating linguistic phenomena that reflect cultural variation . aaron e. duan and j. nà: "the complexity of the language model is a major challenge for LLMs" |
| Approach: | They introduce demonstratives as a probe for grounded knowledge by analyzing 6,400 responses from 320 native speakers. |
| Outcome: | The proposed model fails to understand proximal–distal contrast and shows no cultural differences . the proposed model is a new probe for evaluating embodied cognition and cultural conventions . |
LangSuit·E: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely on language descriptions as inputs. |
| Approach: | They propose a flexible and simulation-free testbed that simulates 6 representative embodied tasks in textual embodies. |
| Outcome: | The proposed testbed offers adaptability to diverse environments without multiple simulation engines and allows easy customization of communication and action strategies. |
Do Large Language Models Know How Much They Know? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models are highly capable systems, but their capabilities and limitations are unclear. |
| Approach: | They develop a benchmark that challenges LLMs to recall all information they possess on specific topics. |
| Outcome: | The proposed model can recall excessive, insufficient, or the precise amount of information they possess on a given topic, indicating their awareness of how much they know about the given topic. |
Evaluating Model Alignment with Human Perception: A Study on Shitsukan in LLMs and LVLMs (2025.coling-main)
Copied to clipboard
| Challenge: | This work examines the alignment of large language models and large vision-language models with human perception. |
| Approach: | They use a dataset of *shitsukan* terms elicited from individuals in response to object images to evaluate their understanding of the Japanese concept of shitukan. |
| Outcome: | The proposed models demonstrated mixed accuracy across benchmark tasks, with limited overlap between model- and human-generated terms. |
Can Language Models Understand Physical Concepts? (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing language models do not understand basic physical concepts in the human world. |
| Approach: | They propose a method to transfer embodied knowledge from visual models to LMs . they use visual concepts and embodies concepts learned from interaction with the world . |
| Outcome: | The proposed method achieves comparable performance with scaling up parameters of LMs 134. |
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
Copied to clipboard
Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |