Release-readiness decision: decoding configuration for a customer-facing summarization feature
You are the on-call ML lead for a B2B product that generates 1–2 sentence “Account Update” summaries from internal CRM notes. A pilot with 200 users shows two failure modes:
- Some summaries are overly short (often 6–10 words) and omit key facts.
- Other summaries are fluent but occasionally include a plausible-sounding detail that is not in the notes.
Constraints: p95 latency must stay under 250 ms; the product team wants consistent tone across runs for the same input, but they also want to avoid repetitive phrasing across different accounts. You can change only decoding-time settings (no retraining, no prompt changes).
You must choose ONE decoding approach to ship this week and specify concrete settings for: (a) the core decoding algorithm (greedy, beam search, top-k sampling, or top-p sampling), (b) whether/how you will use temperature scaling, and (c) whether/how you will apply a length penalty. In your answer, justify how your choices jointly address both failure modes and the stated constraints, including at least one tradeoff you are accepting.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Data Science
Related
Mathematical Justification for Greedy Search
Construction of the Optimal Sequence in Greedy Search
Candidate Set in Greedy Search
A language model is generating a two-token sequence. At the first step, it calculates the probability for the next token: 'Token A' has a probability of 0.6, and 'Token B' has a probability of 0.4. If the model chooses 'Token A', the most probable subsequent token is 'Token C' (with a conditional probability of 0.5). If the model had chosen 'Token B', the most probable subsequent token would be 'Token D' (with a conditional probability of 0.9). A text generation algorithm is used that, at every step, commits to the single token with the highest immediate probability. Based on this process, which sequence will be generated and why?
Algorithm Suitability for Text Generation Tasks
When generating a sequence of text, an algorithm that selects the single most probable token at each step is guaranteed to produce the overall most probable sequence.
Analyzing Suboptimal Outcomes in Text Generation
Selecting and Justifying a Decoding Policy for Two Production Use Cases
Debugging Decoding: Balancing Determinism, Diversity, and Length in a Regulated Product
Post-incident analysis: fixing repetition and truncation by tuning decoding
Choosing a Decoding Configuration Under Latency, Diversity, and Length Constraints
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Beam search
Beam Width (K)
Top-K Token Selection in Beam Search
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Step 1: The initial two sequences being tracked are:
- Sequence 1: "The" (Score: -0.5)
- Sequence 2: "A" (Score: -0.9)
Step 2: The model calculates the log-probabilities for the next possible words for each sequence:
- Expanding "The":
- "cat": -0.8
- "dog": -1.1
- Expanding "A":
- "mouse": -0.2
- "lion": -1.5
Analyzing Search Algorithm Behavior
Diagnosing a Flaw in Sequence Generation
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Selecting and Justifying a Decoding Policy for Two Production Use Cases
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Top-k Sampling Process
Comparison of Top-p and Top-k Sampling
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mat(0.45),rug(0.25),floor(0.15),table(0.10), andwindow(0.03). If the model uses a decoding strategy where it first identifies the 3 most probable tokens and then randomly samples one token from only that reduced group, which of the following statements is true?Effect of Candidate Pool Size on Text Generation
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argTopK Function
Definition of the Top-k Selection Pool
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Softmax Renormalization in Top-k Sampling
Ranking and Top-p (Nucleus) Sampling Process
Comparison of Top-p and Top-k Sampling
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Dynamic Candidate Set in Probabilistic Text Generation
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Balancing Randomness and Coherence in Token Sampling
Using Temperature with Softmax to Control Randomness in Token Selection
Token Sampling from a Conditional Probability Distribution
A language model is calculating the next token's probability distribution over a set of four candidate tokens. The raw output scores (logits) for these tokens are: {Token A: 4.0, Token B: 3.8, Token C: 1.5, Token D: 1.2}. The current generation process uses a temperature parameter
β = 1.0. A developer wants to modify the process to make the model's output less predictable and increase the likelihood of selecting Token B relative to Token A. Which of the following adjustments to the temperature parameterβwould best achieve this goal?Effect of Temperature on Probability Distributions
Parameter Tuning for Text Generation Tasks
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Analyzing the Impact of Length Penalty Variations
Evaluating the Application of Output Length Controls
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