Psychometric Predictive Power of Large Language Models (2024)

Abstract: Instruction tuning aligns the response of large language models (LLMs) with human preferences.Despite such efforts in human–LLM alignment, we find that instruction tuning does not always make LLMs human-like from a cognitive modeling perspective. More specifically, next-word probabilities estimated by instruction-tuned LLMs are often worse at simulating human reading behavior than those estimated by base LLMs.In addition, we explore prompting methodologies for simulating human reading behavior with LLMs. Our results show that prompts reflecting a particular linguistic hypothesis improve psychometric predictive power, but are still inferior to small base models.These findings highlight that recent advancements in LLMs, i.e., instruction tuning and prompting, do not offer better estimates than direct probability measurements from base LLMs in cognitive modeling. In other words, pure next-word probability remains a strong predictor for human reading behavior, even in the age of LLMs.

## introduction aligning computational models with human perception/cognition has historically been a pivotal approach to understanding humans @xcite . with this in mind, computational psycholinguistics has investigated the model of human sentence processing @xcite and recently found an intriguing correlation between next-word probabilities from language models (lms) and human reading behavior-the less predictable a word is, the greater the cognitive load (e.g., longer reading time) humans exhibit-suggesting the expectationbased account of human sentence processing @xcite . based on this finding, the field has further investigated which types of models/algorithms can compute probabilities predict next-word to be preferred by humans better aligned with human reading behavior (figure 1 ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; inter alia). in the field of natural language processing (nlp), in contrast, large language models (llms) tuned to human-preferred responses (e.g., gpt-3.5) improve in performance across a wide range of applications @xcite . given the increasing prevalence of such human-aligned, instructiontuned llms (it-llms), the following computational psycholinguistic question naturally arises: do it-llms successfully simulate human reading behavior in terms of predicted surprisal? the answer to this question is not immediately obvious. on the one hand, the answer might be yes since these are tuned to human-preferred responses @xcite ), which will be, broadly speaking, more aligned with human-like expectations of upcoming information during reading, e.g., not expect fabricated/hallucinated information during reading @xcite . moreover, some it-llms employ reinforcement learning from human feedback (rlhf); such a scenario of language learning through (approximate) human feedback is more plausible than through text alone @xcite , and thus may enhance their cognitive plausibility. on the other hand, it-llms are a step beyond base lms-pure statistical models of plausible text based on large natural language corpora-in the sense that it-llms are tuned to specific humanpreferred responses and may suffer from amplified reporting biases. if the core of human sentence processing is explained by next-word probabilities @xcite , instruction tuning will be unnecessary in simulating human reading behavior. based on this logic, the answer would be no. our experiments show that it-llms frequently yield worse psychometric predictive power (ppp) for human reading behavior than base llms ( §3). this suggests that the current paradigm of instruction tuning is irrelevant to modeling human sentence processing; it-llms are not specially aligned with human language processing, at least based on reading time modeling, despite their ultimate goal of human-machine alignment. furthermore, we address open questions regarding prompting in cognitive modeling: (i) can prompting result in probabilities from it-llms being more aligned with human reading behavior? and (ii) which is better at simulating human reading behavior, using direct probability measurements or directly asking it-llms about the processing cost, e.g., metalinguistic prompting @xcite ? for the first question, we find prompts in line with so-called "good-enough" human sentence processing @xcite to work well. however, these are still worse than smaller base llms ( §4). for the second question, we find metalinguistic prompting to be inferior to direct probability measurement in terms of ppp ( §5). in sum, despite the recent advancements in it-llms and prompting, they do not currently offer better estimates of human reading behavior than simple probability measurements from base llms. this also underlines the value of access to probabilistic outputs for closed-source llms to further the study of cognitive modeling. ## experiment 1: ppp of llms we first observe the ppp of base llms ( §3.1) and then analyze the ppp of it-llms ( §3.2). we explore prompting in §4 and §5. ## experiment 2: prompting the use of it-llms stimulates an additional question-does prompt tuning lead to better ppp for it-llms? one can control the prior belief of llms about upcoming words through the prompt, e.g., complete the sentence to make it grammatically simple. analyzing effective prompt types may shed light on underlying bias in human expectation7 to handle the concern of it-llms simply being confused for a given sentence fragment @xmath0, we re-conducted the experiments with the explicit instruction to predict the next word, yielding results consistent with the original; that is, the results hold up even after instructing it-llms to behave as base llms (appendix b). id prompt syn↓ please complete the following sentence to make it as grammatically simple as possible: \n w0, • • • , wt-1 @xmath1: \n w0, • • • , wt-1 syn↑ please complete the following sentence to make it as grammatically complex as possible: \n w0, • • • , wt-1 lex↓ please complete the following sentence using the simplest vocabulary possible: \n w0, • • • , wt-1 @xmath2: \n w0, • • • , wt-1 lex↑ please complete the following sentence using the most difficult vocabulary possible: \n w0, • • • , wt-1 task1 please complete the following sentence in a human-like manner. it has been reported that human ability to predict next words is weaker than language models and that humans often make noisy predictions, such as careless grammatical errors. \n w0, • • • , wt-1 task2 please complete the following sentence. we are trying to reproduce human reading times with the word prediction probabilities you calculate, so please predict the next word like a human. it has been reported that human ability to predict next words is weaker than language models and that humans often make noisy predictions, such as careless grammatical errors. \n w0, • • • , wt-1 base please complete the following sentence: \n w0, • • • , wt-1 settings: we examine the nine prompts shown in ## experiment 3: metalinguistic prompting instead of using direct probability measurements, one can also ask it-llms about the processing cost of words via metalinguistic prompting @xcite , e.g., please estimate the cognitive load of this word in context. we explore such an approach in this section. settings: based on preliminary experiments, asking a model to predict ther eading time for each word via prompting does not work well. thus, we make the problem simpler: rank words in a sentence in order of processing difficulty-which word in this sentence incurs a higher cognitive load during reading? please order the words (high to low cost). we ask such a question to the model, then calculate spearman's rank correlation between the estimated word processing costs and their actual reading time in each sentence. 9 then, these correlation scores are averaged across the sentences in the reading time corpus; a high score indicates the model's estimated being more aligned with reading time. 10 we employed a 3-shot setting, 11 and ran three different runs with different exemplars for prompting. the average and standard deviation scores across the three runs are reported ( ## conclusion we investigated the ppp of instruction-tuned llms, given their popularity in nlp. we found that it-llms yield worse ppp than base llms with equivalent perplexity, demonstrating the ineffectiveness of current instruction tuning and (metalinguistic) prompting in simulating human reading behavior. one important area of future work is to explore why the current instruction-tuning paradigm is ineffective for modeling human reading behavior, and we highlighted the direction as aligning llms with the human cognition/perception, which has historically been a scientific approach to understanding humans, a.k.a. cognitive modeling, as well as with practical chat agents.