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David Schlangen

20 accepted papers

2025

LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

ACL 2025short

There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reproducibility in the case of proprietary models. We provide JUDGE-BENCH, an extensible collection of 20 NLP datasets with hum…

2025

Playpen: An Environment for Exploring Learning From Dialogue Game Feedback

EMNLP 2025

Interaction between learner and feedback-giver has come into focus recently for post-training of Large Language Models (LLMs), through the use of reward models that judge the appropriateness of a model’s response. In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed a

2025

Representations of Fact, Fiction and Forecast in Large Language Models: Epistemics and Attitudes

ACL 2025long

Rational speakers are supposed to know what they know and what they do not know, and to generate expressions matching the strength of evidence. In contrast, it is still a challenge for current large language models to generate corresponding utterances based on the assessment of facts and confidence…

Cited by 0SourcePDFScholar
2025

Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests

EMNLP 2025

We examine three evaluation paradigms: standard benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory or theory of mind). First, we investigate which of the former two—benchmarks or games—is most effective at discriminatin

2025

Using Game Play to Investigate Multimodal and Conversational Grounding in Large Multimodal Models

COLING 2025main

While the situation has improved for text-only models, it again seems to be the case currently that multimodal (text and image) models develop faster than ways to evaluate them. In this paper, we bring a recently developed evaluation paradigm from text models to multimodal models, namely evaluation…

2024

Conceptual Pacts for Reference Resolution Using Small, Dynamically Constructed Language Models: A Study in Puzzle Building Dialogues

COLING 2024main

Using Brennan and Clark’s theory of a Conceptual Pact, that when interlocutors agree on a name for an object, they are forming a temporary agreement on how to conceptualize that object, we present an extension to a simple reference resolver which simulates this process over time with different conve…

2024

Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft

EMNLP 2024finding

In the Minecraft Collaborative Building Task, two players collaborate: an Architect (A) provides instructions to a Builder (B) to assemble a specified structure using 3D blocks. In this work, we investigate the use of large language models (LLMs) to predict the sequence of actions taken by the Build…

Cited by 2SourcePDFScholar
2024

Sharing the Cost of Success: A Game for Evaluating and Learning Collaborative Multi-Agent Instruction Giving and Following Policies

COLING 2024main

In collaborative goal-oriented settings, the participants are not only interested in achieving a successful outcome, but do also implicitly negotiate the effort they put into the interaction (by adapting to each other). In this work, we propose a challenging interactive reference game that requires…

2024

When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality

ACL 2024long

Incremental models that process sentences one token at a time will sometimes encounter points where more than one interpretation is possible. Causal models are forced to output one interpretation and continue, whereas models that can revise may edit their previous output as the ambiguity is resolved…

2023

Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks

ACL 2023findings

Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. While being actively researched, multimodal models that can additionally handle images as input have yet to catch up in size and generality with language-only models.…

2023

On General Language Understanding

EMNLP 2023short findings

Natural Language Processing prides itself to be an empirically-minded, if not outright empiricist field, and yet lately it seems to get itself into essentialist debates on issues of meaning and measurement ("Do Large Language Models Understand Language, And If So, How Much?"). This is not by accide…

Cited by 0SourceScholar
2023

TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model

ACL 2023findings

Language is by its very nature incremental in how it is produced and processed. This property can be exploited by NLP systems to produce fast responses, which has been shown to be beneficial for real-time interactive applications. Recent neural network-based approaches for incremental processing mai…

2023

Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers

ACL 2023findings

The ability to pick up on language signals in an ongoing interaction is crucial for future machine learning models to collaborate and interact with humans naturally. In this paper, we present an initial study that evaluates intra-episodic feedback given in a collaborative setting. We use a referenti…

2023

clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents

EMNLP 2023long main

Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents" --- agents that operate in rich linguistic and non-linguistic contexts --- through testing them in carefully constructed interactive settings. Other recent work has argued that Large Lang…

Cited by 62SourcecodeScholar
2022

Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared Knowledge

ACL 2022short

Cognitively plausible visual dialogue models should keep a mental scoreboard of shared established facts in the dialogue context. We propose a theory-based evaluation method for investigating to what degree models pretrained on the VisDial dataset incrementally build representations that appropriate…

2022

New or Old? Exploring How Pre-Trained Language Models Represent Discourse Entities

COLING 2022main

Recent research shows that pre-trained language models, built to generate text conditioned on some context, learn to encode syntactic knowledge to a certain degree. This has motivated researchers to move beyond the sentence-level and look into their ability to encode less studied discourse-level phe…

2021

Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models

NAACL 2021long

Coherent discourse is distinguished from a mere collection of utterances by the satisfaction of a diverse set of constraints, for example choice of expression, logical relation between denoted events, and implicit compatibility with world-knowledge. Do neural language models encode such constraints?…

2021

Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer

ACL 2021long

To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context. This often comes in the form of knowledge graphs, and the integration is done by creating pseudo utterances through paraphra…

2021

Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU

EMNLP 2021main

Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents. The currently popular Transformer architecture inherently processes sequences as a whole, abstracting away the notion of time. Recent work attempts to apply Tr…