Quantifying Laziness, Decoding Suboptimality, and Context Degradation in Large Language Models
📝 Abstract
Large Language Models (LLMs) often exhibit behavioral artifacts such as laziness (premature truncation of responses or partial compliance with multi-part requests), decoding suboptimality (failure to select higher-quality sequences due to myopic decoding), and context degradation (forgetting or ignoring core instructions over long conversations). We conducted three controlled experiments (A, B, and C) to quantify these phenomena across several advanced LLMs (OpenAI GPT-4 variant, DeepSeek). Our results indicate widespread laziness in satisfying complex multi-part instructions: models frequently omitted required sections or failed to meet length requirements despite explicit prompting. However, we found limited evidence of decoding suboptimality in a simple reasoning task (the models’ greedy answers appeared to align with their highest-confidence solution), and we observed surprising robustness against context degradation in a 200-turn chaotic conversation test - the models maintained key facts and instructions far better than expected. These findings suggest that while compliance with detailed instructions remains an open challenge, modern LLMs may internally mitigate some hypothesized failure modes (such as context forgetting) in straightforward retrieval scenarios. We discuss implications for reliability, relate our findings to prior work on instruction-following and long-context processing, and recommend strategies (such as self-refinement and dynamic prompting) to reduce laziness and bolster multi-instruction compliance.
💡 Analysis
Large Language Models (LLMs) often exhibit behavioral artifacts such as laziness (premature truncation of responses or partial compliance with multi-part requests), decoding suboptimality (failure to select higher-quality sequences due to myopic decoding), and context degradation (forgetting or ignoring core instructions over long conversations). We conducted three controlled experiments (A, B, and C) to quantify these phenomena across several advanced LLMs (OpenAI GPT-4 variant, DeepSeek). Our results indicate widespread laziness in satisfying complex multi-part instructions: models frequently omitted required sections or failed to meet length requirements despite explicit prompting. However, we found limited evidence of decoding suboptimality in a simple reasoning task (the models’ greedy answers appeared to align with their highest-confidence solution), and we observed surprising robustness against context degradation in a 200-turn chaotic conversation test - the models maintained key facts and instructions far better than expected. These findings suggest that while compliance with detailed instructions remains an open challenge, modern LLMs may internally mitigate some hypothesized failure modes (such as context forgetting) in straightforward retrieval scenarios. We discuss implications for reliability, relate our findings to prior work on instruction-following and long-context processing, and recommend strategies (such as self-refinement and dynamic prompting) to reduce laziness and bolster multi-instruction compliance.
📄 Content
Large Language Models (LLMs) have rapidly become integrated into complex workflows and high-stakes applications in education, medicine, science, and beyond (Zhou et al., 2024). With this increased use, ensuring the reliability and faithfulness of model behavior has become paramount. Users and researchers have reported several recurring issues in extended interactions with LLMs. One commonly observed issue is that models sometimes “get lazy” -that is, they produce responses that are shorter or less detailed than requested, or they ignore certain instructions in a multi-part query. Another concern is that models might forget earlier instructions or facts in a long conversation, exhibiting degraded performance as the dialogue progresses. A third hypothesis is that LLMs might be choosing suboptimal responses due to limitations in the decoding strategy (for example, a greedy one-pass generation might miss a better solution that the model actually assigns higher probability to).
These failure modes have started to receive attention in the research community. The tendency of LLMs to not follow all given instructions in complex prompts has been quantified in recent studies. Harada et al. (2024) show that as the number of instructions in a prompt increases, models’ ability to satisfy all of them drops off significantly (Harada et al., 2024). In fact, they term this the “curse of instructions,” finding that even state-of-the-art models struggled when prompts contained many requirements simultaneously (e.g. 10 distinct directives) -the success rate of fulfilling every instruction decays roughly exponentially with the number of instructions. This suggests that instruction-following compliance does not scale gracefully; even if a model is very capable on each individual sub-task, ensuring it handles multiple constraints or sub-tasks in one query is a challenge. Our notion of “laziness” in this paper relates to this phenomenon: the model opts to address only some parts of a query or gives a shorter-than-required answer, effectively truncating the effort. Such behavior might stem from models being overly optimized for conciseness or safety, or simply from an inherent limitation in juggling multiple objectives.
Prior alignment work indeed noted that instruction-tuned models can have difficulty balancing competing instructions or lengthy requests. We aim to quantitatively measure how severe this issue is across different LLMs and prompt types.
The second issue, which we term decoding suboptimality, deals with the possibility that an LLM’s decoding procedure (often greedy or sampling-based generation) may fail to find a solution that the model “knows” to be better. In other words, there might exist a higherprobability or higher-quality completion in the model’s distribution that is missed due to search limitations. This concept connects to earlier findings in language generation research. For instance, Holtzman et al. (2020) demonstrated that standard maximum-likelihood decoding (greedy or beam search) can lead to degenerate, repetitive text -indicating that the true argmax of a model’s distribution is often an undesirable output [5]. Techniques like nucleus sampling were proposed to avoid such local optima (Holtzman et al., 2020). In the context of reasoning or question-answering, others have shown that sampling multiple different reasoning paths and then selecting the most consistent answer (a process known as self-consistency) yields better results than a single greedy run. Wang et al. (2023) introduced self-consistency decoding precisely to address the scenario where a model might internally assign high probability to a correct answer that is only reachable via a non-greedy chain-of-thought; by sampling many chains of thought, one can uncover those better answers that a single pass might miss. These advances suggest that large models do have latent knowledge or reasoning capacity that isn’t always realized in a straightforward greedy output. We test a simplified version of this idea: given a known correct solution to a problem, is the model internally assigning it a higher likelihood than the answer it would normally output? A positive result would indicate a kind of decoding suboptimality -the model “knew” a better answer but didn’t produce it by default. Prior work in chain-of-thought prompting (Wei et al., 2022;Kojima et al., 2022) also relates to this: those works found that adding intermediate reasoning steps or specific prompts could coax the model into providing more correct answers, implying that the initially preferred answer without such prompting was suboptimal in many cases.
The third issue is context degradation over long interactions. LLMs have a fixed context window and do not possess a true long-term memory -they process each new prompt along with a window of recent dialogue. If a conversation becomes very long or contains a lot of irrelevant (distracting) text, the concern is that the model’s perform
This content is AI-processed based on ArXiv data.