Challenges in GPT-3 logprobs calculation for word surprisal: incorrect tokens and surprising results for multi-token phrases

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1

I am attempting to calculate word surprisal values using OpenAI’s Completions API, but I have encountered multiple issues.

Surprisal represents how surprising a word is based on the preceding context. For instance:

  1. The dog barked at the moon (high surprisal - unexpected)
  2. The dog barked at the cat (low surprisal - expected)

I utilize client.completions.create() with echo=True and logprobs=1 to obtain token probabilities. My input sentences feature target words enclosed in brackets like [word]. During processing, I substitute the brackets with a distinct marker (β), tokenize the sentence using the GPT-2 tokenizer, retrieve logprobs for the targeted tokens, and compute their surprisal.

import os
from transformers import AutoTokenizer
import numpy as np
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
tokenizer = AutoTokenizer.from_pretrained("gpt2")

def compute_surprisal(sentence, model_name):
    # Replace brackets with marker
    adjusted_sentence = sentence.replace("[", "β").replace("] ", "β ")
    encoded_ids = tokenizer.encode(adjusted_sentence)
    
    # Identify marker positions
    marker_indices = np.where(
        (np.array(encoded_ids) == tokenizer.encode("β")[-1])
    )[0]
    
    context_tokens = encoded_ids[:marker_indices[0]]
    target_tokens = encoded_ids[marker_indices[0] + 1 : marker_indices[1]]
    
    clean_sentence = sentence.replace("[", "").replace("]", "")
    
    api_response = client.completions.create(
        model=model_name,
        prompt=clean_sentence,
        max_tokens=0,
        temperature=0,
        logprobs=1,
        echo=True
    )
    
    logprob_data = api_response.choices[0].logprobs.token_logprobs
    targeted_logprobs = logprob_data[len(context_tokens):len(context_tokens) + len(target_tokens)]
    
    surprisal_value = -np.sum(targeted_logprobs) / np.log(2)
    return surprisal_value

Issues I am facing:

  1. Incorrect token extraction - Occasionally, it selects the wrong token. For instance, in “…at the [train station]…”, it calculates surprisal for “the” instead of “station”.

  2. Zero results - Some target words yield a surprisal of -0.0 despite appearing correctly identified.

  3. Unexpected outcomes with multi-token expressions - Experiencing counterintuitive results like:

    • “Emily bought some [hand cream]” = higher surprisal
    • “Gary bought some [hand cream]” = lower surprisal

    This seems inconsistent since the term “hand cream” should be more anticipated in connection with “Emily”.

I suspect the complications stem from handling multi-token entries, as the initial script was designed for single tokens exclusively. Has anyone experienced similar challenges when calculating surprisal with OpenAI’s API?

2

I’ve hit this exact token alignment nightmare working with language models at scale. You’re manually syncing tokenization between different systems - that’s why it keeps breaking.

Stop doing this calculation by hand. Automate the whole pipeline instead. I built something similar last year and ditched all the manual token parsing headaches by automating everything.

Here’s what you actually need:

  • System takes bracketed sentences as input
  • Handles API calls with proper error handling
  • Auto-manages tokenization alignment
  • Batches surprisal calculations
  • Dumps results in structured format

Your multi-token weirdness happens because context matters way more than you think. “Emily” vs “Gary” shifts probability distributions for the entire sequence, not just your target phrase.

I solve this by building automated workflows that handle edge cases and run continuously. No more manual token counting or alignment debugging.

Set up the surprisal pipeline as an automated workflow - it’ll process sentences, call OpenAI properly, and give you clean results without the manual debugging hell.

3

Your marker approach is breaking the tokenization flow. Had the same problem when I built a surprisal calculator for research. The β marker gets tokenized in ways you don’t expect, which screws up everything downstream. Ditch the markers completely. Make two API calls instead - one with just context, then another with context plus target. Compare the logprobs between calls to isolate your target tokens. No more alignment headaches. The Emily vs Gary results aren’t bugs - they’re right. Gender associations actually do affect probability distributions for product categories. “Hand cream” really does have different likelihoods after female vs male names because that’s what the model learned from training data. These are real biases from real text patterns. Always print the raw tokens from the API before you calculate anything. You’ll catch when “train station” becomes [“train”, “Ġstation”] or other weird splits that break your indexing.

4

Been through this token alignment hell myself when building surprisal analysis for production systems. Your bracket replacement is creating a cascade of problems.

Stop manually syncing tokenizers and hunting alignment bugs. Automate the whole thing. I handle hundreds of surprisal calculations daily without touching token indices manually.

Here’s what actually works:

Build an automated flow that processes sentence batches, handles OpenAI API calls with proper retry logic, and manages tokenization complexity behind the scenes. No more debugging why “train station” becomes the wrong tokens.

Your Emily vs Gary confusion? That’s correct behavior. Names carry gender associations that shift probability distributions for product categories. The model learned these patterns from training data, so “hand cream” genuinely has different likelihoods based on context.

Zero surprisal happens when you hit None values in logprobs that aren’t filtered out before the sum operation.

Ditch manual token alignment. Set up an automated surprisal pipeline that handles edge cases, processes sentences in batches, and gives you clean results without the debugging nightmare.

5

i dealt with this exact issue in my nlp project last semester! zero surprisal usually means you’re hitting padding tokens or the api’s returning none for certain positions. check if your targeted_logprobs are none before you sum them. the misalignment’s probably because you’re using gpt-2 tokenizer locally but openai uses something different internally. log your encoded_ids next to the actual tokens from the api - you’ll see where they split.

6

I’ve dealt with the same alignment headaches using GPT-3 logprobs. Your bracket method screws up tokenization because your local GPT-2 tokenizer doesn’t match what OpenAI uses.

Here’s what works better: send the clean sentence to the API first, get the full token sequence back, then find your target tokens by position in that array. No more guessing at alignment.

For the multi-token surprisal mess - yeah, that’s subword tokenization biting you. “Station” gets chopped into multiple tokens and your indexing grabs the wrong bits. The Emily vs Gary thing actually makes sense - different names create different context expectations that shift the whole probability distribution.

What saved me was adding validation checks. Print out the actual tokens before calculating surprisal - you’ll see “train station” tokenized as “train”, " station" (with leading spaces) or other weird splits you didn’t expect.