From Templates to Natural Language: Generalization Challenges in Instruction-Tuned LLMs for Spatial Reasoning

Instruction-tuned large language models (LLMs) have shown strong performance on a variety of tasks; however, generalizing from synthetic to human-authored instructions in grounded environments remains a challenge for them. In this work, we study generalization challenges in spatial grounding tasks where models interpret and translate instructions for building object arrangements on a $2.5$D grid. We fine-tune LLMs using only synthetic instructions and evaluate their performance on a benchmark dataset containing both synthetic and human-written instructions. Our results reveal that while models generalize well on simple tasks, their performance degrades significantly on more complex tasks. We present a detailed error analysis of the gaps in instruction generalization.

clem:todd: A Framework for the Systematic Benchmarking of LLM-Based Task-Oriented Dialogue System Realisations

The emergence of instruction-tuned large language models (LLMs) has advanced the field of dialogue systems, enabling both realistic user simulations and robust multi-turn conversational agents. However, existing research often evaluates these components in isolation-either focusing on a single user simulator or a specific system design-limiting the generalisability of insights across architectures and configurations. In this work, we propose clem todd (chat-optimized LLMs for task-oriented dialogue systems development), a flexible framework for systematically evaluating dialogue systems under consistent conditions. clem todd enables detailed benchmarking across combinations of user simulators and dialogue systems, whether existing models from literature or newly developed ones. It supports plug-and-play integration and ensures uniform datasets, evaluation metrics, and computational constraints. We showcase clem todd's flexibility by re-evaluating existing task-oriented dialogue systems within this unified setup and integrating three newly proposed dialogue systems into the same evaluation pipeline. Our results provide actionable insights into how architecture, scale, and prompting strategies affect dialogue performance, offering practical guidance for building efficient and effective conversational AI systems.

Playpen: An Environment for Exploring Learning Through Conversational Interaction

Are we running out of learning signal? Predicting the next word in an existing text has turned out to be a powerful signal, at least at scale. But there are signs that we are running out of this resource. In recent months, interaction between learner and feedback-giver has come into focus, both for "alignment" (with a reward model judging the quality of instruction following attempts) and for improving "reasoning" (process- and outcome-based verifiers judging reasoning steps). In this paper, we explore to what extent synthetic interaction in what we call Dialogue Games -- goal-directed and rule-governed activities driven predominantly by verbal actions -- can provide a learning signal, and how this signal can be used. We introduce an environment for producing such interaction data (with the help of a Large Language Model as counterpart to the learner model), both offline and online. We investigate the effects of supervised fine-tuning on this data, as well as reinforcement learning setups such as DPO, and GRPO; showing that all of these approaches achieve some improvements in in-domain games, but only GRPO demonstrates the ability to generalise to out-of-domain games as well as retain competitive performance in reference-based tasks. We release the framework and the baseline training setups in the hope that this can foster research in this promising new direction.

Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests

We examine three evaluation paradigms: large question-answering 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 discriminating LLMs of varying quality. Then, inspired by human cognitive assessments, we compile a suite of targeted tests that measure cognitive abilities deemed essential for effective language use, and we investigate their correlation with model performance in benchmarks and games. Our analyses reveal that interactive games are superior to standard benchmarks in discriminating models. Causal and logical reasoning correlate with both static and interactive tests, while differences emerge regarding core executive functions and social/emotional skills, which correlate more with games. We advocate the development of new interactive benchmarks and targeted cognitive tasks inspired by assessing human abilities but designed specifically for LLMs.

Plant in Cupboard, Orange on Table, Book on Shelf. Benchmarking Practical Reasoning and Situation Modelling in a Text-Simulated Situated Environment

Large language models (LLMs) have risen to prominence as 'chatbots' for users to interact via natural language. However, their abilities to capture common-sense knowledge make them seem promising as language-based planners of situated or embodied action as well. We have implemented a simple text-based environment -- similar to others that have before been used for reinforcement-learning of agents -- that simulates, very abstractly, a household setting. We use this environment and the detailed error-tracking capabilities we implemented for targeted benchmarking of LLMs on the problem of practical reasoning: Going from goals and observations to actions. Our findings show that environmental complexity and game restrictions hamper performance, and concise action planning is demanding for current LLMs.

Ad-hoc Concept Forming in the Game Codenames as a Means for Evaluating Large Language Models

This study utilizes the game Codenames as a benchmarking tool to evaluate large language models (LLMs) with respect to specific linguistic and cognitive skills. LLMs play each side of the game, where one side generates a clue word covering several target words and the other guesses those target words. We designed various experiments by controlling the choice of words (abstract vs. concrete words, ambiguous vs. monosemic) or the opponent (programmed to be faster or slower in revealing words). Recent commercial and open-weight models were compared side-by-side to find out factors affecting their performance. The evaluation reveals details about their strategies, challenging cases, and limitations of LLMs.

Incremental Dialogue Management: Survey, Discussion, and Implications for HRI

Efforts towards endowing robots with the ability to speak have benefited from recent advancements in NLP, in particular large language models. However, as powerful as current models have become, they still operate on sentence or multi-sentence level input, not on the word-by-word input that humans operate on, affecting the degree of responsiveness that they offer, which is critical in situations where humans interact with robots using speech. In this paper, we review the literature on interactive systems that operate incrementally (i.e., at the word level or below it). We motivate the need for incremental systems, survey incremental modeling of important aspects of dialogue like speech recognition and language generation. Primary focus is on the part of the system that makes decisions, known as the dialogue manager. We find that there is very little research on incremental dialogue management, offer some requirements for practical incremental dialogue management, and the implications of incremental dialogue for embodied, robotic platforms.

Towards No-Code Programming of Cobots: Experiments with Code Synthesis by Large Code Models for Conversational Programming

While there has been a lot of research recently on robots in household environments, at the present time, most robots in existence can be found on shop floors, and most interactions between humans and robots happen there. ``Collaborative robots'' (cobots) designed to work alongside humans on assembly lines traditionally require expert programming, limiting ability to make changes, or manual guidance, limiting expressivity of the resulting programs. To address these limitations, we explore using Large Language Models (LLMs), and in particular, their abilities of doing in-context learning, for conversational code generation. As a first step, we define RATS, the ``Repetitive Assembly Task'', a 2D building task designed to lay the foundation for simulating industry assembly scenarios. In this task, a `programmer' instructs a cobot, using natural language, on how a certain assembly is to be built; that is, the programmer induces a program, through natural language. We create a dataset that pairs target structures with various example instructions (human-authored, template-based, and model-generated) and example code. With this, we systematically evaluate the capabilities of state-of-the-art LLMs for synthesising this kind of code, given in-context examples. Evaluating in a simulated environment, we find that LLMs are capable of generating accurate `first order code' (instruction sequences), but have problems producing `higher-order code' (abstractions such as functions, or use of loops).

The Unreasonable Ineffectiveness of Nucleus Sampling on Mitigating Text Memorization

This work analyses the text memorization behavior of large language models (LLMs) when subjected to nucleus sampling. Stochastic decoding methods like nucleus sampling are typically applied to overcome issues such as monotonous and repetitive text generation, which are often observed with maximization-based decoding techniques. We hypothesize that nucleus sampling might also reduce the occurrence of memorization patterns, because it could lead to the selection of tokens outside the memorized sequence. To test this hypothesis we create a diagnostic dataset with a known distribution of duplicates that gives us some control over the likelihood of memorization of certain parts of the training data. Our analysis of two GPT-Neo models fine-tuned on this dataset interestingly shows that (i) an increase of the nucleus size reduces memorization only modestly, and (ii) even when models do not engage in "hard" memorization -- a verbatim reproduction of training samples -- they may still display "soft" memorization whereby they generate outputs that echo the training data but without a complete one-by-one resemblance.

LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluation

Natural Language Processing has moved rather quickly from modelling specific tasks to taking more general pre-trained models and fine-tuning them for specific tasks, to a point where we now have what appear to be inherently generalist models. This paper argues that the resultant loss of clarity on what these models model leads to metaphors like "artificial general intelligences" that are not helpful for evaluating their strengths and weaknesses. The proposal is to see their generality, and their potential value, in their ability to approximate specialist function, based on a natural language specification. This framing brings to the fore questions of the quality of the approximation, but beyond that, also questions of discoverability, stability, and protectability of these functions. As the paper will show, this framing hence brings together in one conceptual framework various aspects of evaluation, both from a practical and a theoretical perspective, as well as questions often relegated to a secondary status (such as "prompt injection" and "jailbreaking").